Electricity Markets

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Electricity markets are platforms where electricity is bought and sold, facilitating the trading of electrical energy among producers, consumers, and intermediaries. These markets operate under various regulatory frameworks and pricing mechanisms, aiming to ensure efficient allocation of resources, reliability of supply, and competitive pricing in the generation and distribution of electricity.

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Key research themes

1. How do distributed energy resources (DERs) and demand response programs transform electricity market structures and operations?

This research area focuses on the integration of distributed energy resources—such as energy storage systems (ESSs), photovoltaic distributed generation (PV DG), and electric vehicles (EVs)—and flexible demand programs into electricity markets. As DERs shift traditional unidirectional power flows to bidirectional flows with active prosumers, they challenge existing market models and market-clearing mechanisms, necessitating new business models, tariff designs, and ancillary service provisions. Demand response programs modify consumer behavior to balance supply-demand and improve grid stability. Understanding these transformations is essential to designing efficient, flexible markets that accommodate DER proliferation and enhance socio-economic welfare.

Electricity Markets in the Context of Distributed Energy Resources and Demand Response Programs: Main Developments and Challenges Based on a Systematic Literature Review

by Benedito D Bonatto

2024, Energies

Key finding: This systematic review reveals that DERs including ESSs, PV DG, and EVs profoundly modify electricity markets by enabling peer-to-peer (P2P) trading, introducing blockchain-enabled transactions, and altering tariff formation... Read more

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Effects of Local Electricity Trading on Power Flows and Voltage Levels for Different Elasticities and Prices

by Ivan Rajšl

2023, Energies

Key finding: This study demonstrates that near real-time local electricity trading (LET) among consumers, producers, and prosumers at the distribution grid level is feasible without excessive computational burden, provided that... Read more

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V2G Participation in European Union: Legal and Regulatory Barriers in Spanish Electricity Markets

by Yusuf Ercan ÖZERCAN

2025, OGEL Energy Law Journal

Key finding: This article articulates how V2G technology, a subset of DERs enhancing bidirectional energy flows between EVs and the grid, is inhibited from effective participation in balancing electricity markets due to regulatory and... Read more

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Adjacent Markets Influence Over Electricity Trading—Iberian Benchmark Study

by Tiago Pinto

2023, Energies

Key finding: This paper reveals that operating across adjacent markets, such as carbon markets and guarantees of origin, amplifies the economic benefits for distributed generation stakeholders. Within the Iberian electricity market, it... Read more

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2. What factors contribute to price dispersion and instability in liberalized electricity markets, and how can market power affect price formation?

This theme examines price dynamics in liberalized electricity markets, focusing on causes of price dispersion, market fragmentation, and the exercise of market power by participants such as aggregators. It analyzes how transmission constraints, market design, and strategic behavior impact locational marginal prices (LMP), leading to volatility and inefficiencies. Understanding these determinants is vital for market regulation, effective price formation, and ensuring competitive equilibrium, especially given the high stakes in consumer welfare and investment signals.

Electricity Markets Instability: Causes of Price Dispersion

by Mykola Kyzym

2021, Sustainability

Key finding: This study analyzes day-ahead electricity price dispersion across 40 bidding zones in 26 European countries, revealing that geographic market orientation, direction of electricity flows, and generation structure fundamentally... Read more

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Opportunities for Price Manipulation by Aggregators in Electricity Markets

by Niangjun Chen

2022, ACM SIGMETRICS Performance Evaluation Review

Key finding: This paper quantifies how aggregators in radial distribution networks can strategically curtail generation to manipulate market prices and maximize profits, facilitated by limited ISO oversight of intermittent resources.... Read more

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A new design for market power monitoring in the electricity market. A simulation for Italy

by Paolo Polinori

2024, Economia Politica

Key finding: Introducing a novel model that integrates supply and demand market power assessments via ex-ante bidding data, this paper uncovers significant dead-weight welfare losses caused by oligopolistic and oligopsonistic behaviors in... Read more

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Price Formation Criteria for Electricity Supply Companies in the Conditions of Market Liberalization

by Iryna Vavdiichyk

2023, Financial and credit activity problems of theory and practice

Key finding: Investigating Ukrainian electricity supply companies under market liberalization conditions, this study identifies marginal costs, no-load costs, and loading costs as core price components. It empirically establishes that... Read more

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3. How can electricity market models be improved to realistically represent price variability and support integration of renewables and new technologies?

This research area addresses shortcomings in electricity market modeling, particularly the underrepresentation of hourly price variability critical for assessing the value of flexible technologies like storage and transmission. Traditional models that assume generators bid short-run marginal costs fail to capture price spreads driven by operational constraints, strategic bidding, and renewables integration. Enhancing models to reflect these complexities is pivotal for accurate revenue estimation, investment decisions, and policy design aimed at a decarbonized electricity sector dominated by zero-marginal-cost renewables.

Getting prices right in structural electricity market models

by Iain Staffell

2019, Energy Policy

Key finding: By extending traditional merit order stack models, this work introduces bid spreading below and above short-run marginal costs to replicate historical hourly wholesale electricity price variability more accurately. These... Read more

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EuroMod: Modelling European power markets with improved price granularity

by Iain Staffell

2024, Energy Economics

Key finding: EuroMod introduces two key enhancements over traditional linear dispatch models: quadratic cost curves allowing bids to diverge from average variable costs, and a post-optimization adjustment of prices relative to net demand... Read more

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Economics of Energy Supply

by Norbert C Edomah

2019, Reference Module in Earth Systems and Environmental Sciences.

Key finding: This reference work outlines fundamental economic principles underpinning energy supply and market economics, emphasizing how global energy demand growth and technology innovation drive energy supply dynamics. It... Read more

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All papers in Electricity Markets

Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

by rosa espinola

2005, IEEE Transactions on Power Systems

Price forecasting has been at the center of intense studies since the introduction of competition in Electricity industry. Price forecasts are a fundamental input to an energy company's decision making and strategy development. The... more

Fig. 2. Actual Vs Predicted price of a typical day during winter. Fig. 1. Actual vs Predicted price of atypical day during spring

Fig. 3. Actual Vs Predicted price of a typical day during summer.

ERROR METRICS USING ROBUST SMOOTHER/CLEANER WAVELET TRANSFROM AND WNN

PREDEICTION USING ROBUST SMOOTHER/CLEANER WAVELET TRANSFORM AND WNN TABLEI

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Stochastic Security-Constrained Unit Commitment

by Lei Wu

2007, IEEE Transactions on Power Systems

This paper presents a stochastic model for the longterm solution of security-constrained unit commitment (SCUC). The proposed approach could be used by vertically integrated utilities as well as the ISOs in electricity markets. In this... more

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Forecasting Next-Day Electricity Prices by Time Series Models

by Javier Contreras

2002, IEEE Power Engineering Review

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to... more

Fig. 2. Forecast of August week in the Spanish market by transfer function. Prices in euros per megawatt hour.

Fig. 1. Forecast of August week in the Spanish market by dynamic regression. Prices in euros per megawatt hour.

Fig. 5. Forecast of April week in the Californian market by dynamic regression. Prices in dollars per megawatt hour. Fig. 3. Forecast of November week in the Spanish market by dynamic regression. Prices in euros per megawatt hour.

Fig. 4. Forecast of November week in the Spanish market by transfer function Prices in euros per megawatt hour.

Fig. 6. Forecast of April week in the Californian market by transfer function. Prices in dollars per megawatt hour.

DAILY MEAN ERRORS OF Aucust WEEK IN THE SPANISH MARKET BY DYNAMIC REGRESSION

DAILY MEAN ERORS OF AUGUST WEEK IN THE SPANISH MARKET BY TRANSFER FUNCTION The SCA system [16] has been used to estimate the parame- ters of the models in Step 1. The parameter estimation is based on maximizing a likelihood function for the available data [12]. A conditional likelihood function (as described in [16]) has been selected in order to get a good starting point to obtain an exact likelihood function (as described in [16]). Also, an option to de- tect and adjust possible outliers has been selected.

DAILY MEAN ERRORS OF NOVEMBER WEEK IN THE SPANISH MARKET BY DYNAMIC REGRESSION DAILY MEAN ERRORS OF NOVEMBER WEEK IN THE SPANISH MARKET BY TRANSFER FUNCTION

DAILY MEAN ERRORS OF APRIL WEEK IN THE CALIFORNIAN MARKET BY TRANSFER FUNCTION The seven daily mean errors for this week are shown in the Table II.

DAILY MEAN ERRORS OF APRIL WEEK IN THE CALIFORNIAN MARKET BY DYNAMIC REGRESSION

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Operation and sizing of energy storage for wind power plants in a market system

by Arne Holen

2003, International Journal of Electrical Power & Energy Systems

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Economics of electric energy storage for energy arbitrage and regulation in New York

by Rahul Walawalkar

Unlike markets for storable commodities, electricity markets depend on the real-time balance of supply and demand. Although much of the present-day grid operates effectively without storage, cost-effective ways of storing electrical... more

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Demand response scheduling by stochastic SCUC

by Masood Parvania

2010, Smart Grid, IEEE …

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Restructuring, Competition and Regulatory Reform in the U.S. Electricity Sector 1

by Paul Joskow

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Day‐ahead price forecasting of electricity markets by a hybrid intelligent system

by Meysam Hemmati

2009, European Transactions on Electrical …

In a competitive electricity market, price forecasts are important for market participants. However, electricity price is a complex signal due to its nonlinearity, nonstationarity, and time variant behavior. In spite of much research in... more

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A market approach to long-term security of supply

by Carlos Vázquez

2002, IEEE Transactions on Power Systems

The problem of ensuring that there is enough generation capacity to meet future demand has been an issue in market design since the beginning of the deregulation process. Although ideally the market itself should be enough to provide... more

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Mascem: A Multiagent System That Simulates Competitive Electricity Markets

by Zita Vale

2003, IEEE Expert / IEEE Intelligent Systems

A g e n t s a n d M a r k e t s

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Energy and reserve pricing in security and network-constrained electricity markets

by jose arroyo

2005, IEEE Transactions on Power Systems

This paper analyzes some unresolved pricing issues in security-constrained electricity markets subject to transmission flow limits. Although the notion of separate reserve types as proposed by FERC can be precisely and unambiguously... more

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Constructing Bidding Curves for a Price-Taking Retailer in the Norwegian Electricity Market

by Stein-Erik Fleten

2005, IEEE Transactions on Power Systems

We propose a stochastic linear programming model for constructing piecewise-linear bidding curves to be submitted to Nord Pool, which is the Nordic power exchange. We consider the case of a price-taking power marketer who supplies... more

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Price Wars and Collusion in the Spanish Electricity Market

by Juan Toro

2004

We analyze the time-series of prices in the Spanish electricity market by means of a time varying-transition-probability Markov switching model. Accounting for changes in demand and cost conditions (which reflect changes in input costs,... more

PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET Figure 1 plots the time series of demand, prices and the estimated marginal costs and price-cost mark-ups in the Spanish electricity market from January 1998 to December 1998.

Figure 2: Endesa’s and Iberdrola’s Market Shares, CTC Shares and Mark-ups, 1998 PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET market shares and CTC shares together with their mark-ups, shows that this is not the

Figure 3: Representative Thermal Cost Curves at the Firm Level (on 27/12/98). PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET igures. Our data set distinguishes between each firm’s daily pondage hydro and run of

Figure 4: Smooth Probabilities of Being in a Price War in Model 1 (trigger is AShareENP PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET all models. Therefore, our results show that there is further information embodied in the This is consistent with Green and Porter’s second main prediction, namely, that price wars

PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET ASM P,_-1 is highly meaningful: in contrast to models of collusion among uncontracted

PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET able generating capacity, and the remaining 20% is divided among two smaller firms -

Table 3: Parameters Estimates of the TVTP Models Note: Standard errors are in parenthesis

Table 4: Specification Tests Table 3 also presents enough evidence to support the hypothesis that two distinct price PRICE WARS AND COLLUSION IN THE SPANISH ELECTRICITY MARKET

Table 5: Dating of Price Wars, Duration, Depth, and Generators’ Mark-ups in the period with respect to the last collusive period -which we refer to as the depth of the price war- Interesting information can also be gathered concerning firms’ mark-ups prior to en-

Table 6: Average Markups during Collusive and Price War Periods (based on Model 1,

and the average marginal effect, This information is provided in Table 7, and it is complemented in Figure 5, which

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Optimal designs of small CHP plants in a market with fluctuating electricity prices

by Henrik Lund

2005, Energy Conversion and Management

Combined Heat and Power production (CHP) are essential for implementation of the climate change response objectives in many countries. In an introduction period, small CHP plants have typically been offered fixed electricity prices, but... more

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Stochastic modeling of financial electricity contracts

by Fred Benth and

2008, Energy Economics

We discuss the modeling of electricity contracts traded in many deregulated power markets. These forward/ futures type contracts deliver (either physically or financially) electricity over a specified time period, and is frequently... more

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Application of Information-Gap Decision Theory to Risk-Constrained Self-Scheduling of GenCos

by Behnam Mohammadi-ivatloo

ieeexplore.ieee.org

In a competitive electricity market, a generation company (GenCo) optimizes its operation schedules, referred to as self-scheduling, in order to maximize its profit. However, various sources of uncertainty, such as market price... more

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Cournot versus Supply Functions: What does the data tell us?

by Bert Willems

2009, Energy Economics

This paper compares two popular models of oligopolistic electricity markets, Cournot and the Supply Function Equilibrium (SFE), and then tests which model best describes the observed market data. Using identical demand and supply... more

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Electricity price forecasting through transfer function models

by Francisco Nogales

2006, Journal of the Operational Research Society

Forecasting electricity prices in presentday competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a... more

Figure 1 Time framework to forecast electricity prices for day d.

Figure 2 Modelling and demand effect: evolution of the median percentage error from 1 July, 2003 to 31 August, 2003. (a) Models 1 and 2 and (b) Models 3 and 4.

Figure 3 Evolution of actual prices (solid line) and confidence intervals (shadow) for price forecasts: first week of July 2003. Acknowledgements—This research was partly supported by the Ministerio de Educacion y Ciencia of Spain, through Projects MTM2004-02334 and DPI2003-01362.

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Mixed models for short-run forecasting of electricity prices: application for the Spanish market

by Carolina García Martos

2007

Abstract—Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate... more

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Modeling, simulation and decision support

by Danial Esmaeili Aliabadi

2021, Local Electricity Markets

Energy markets are intertwined and complex systems that influence and are affected by various sectors such as transportation, industry, and electric power. Solely focusing on the power sector without considering the up- and downstream of... more

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Combined hydro-wind generation bids in a pool-based electricity market

by Jorge Crespo

2009, Electric Power Systems Research

a b s t r a c t Present regulatory trends are promoting the direct participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment... more

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Demand Response Exchange in the Stochastic Day-Ahead Scheduling With Variable Renewable Generation

by Abdullah Abusorrah

2015, IEEE Transactions on Sustainable Energy

This paper proposes a pool-based demand response exchange (DRX) model in which economic demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load... more

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The effect of wind generation and weekday on Spanish electricity spot price forecasting

by rosa espinola

2011, Lancet

This paper empirically compares the predictive accuracy of a set of methods for day-ahead spot price forecasting in the Spanish electricity market. The methods come from time series analysis and artificial intelligence disciplines, and... more

Fig. 1. Spanish day-ahead electricity market prices, and load and wind generation forecasts made by the system operator. prices, as weighted nearest neighbors in (Troncoso, Riquelme et al. 2007). cope with this task. For instance, an Elman recurrent neural network is used in (Andalib, Safavieh et al

Fig. 2. Daily (solid line) and weekly (dash line) components of the holt-winters decomposition. B. Exponential smoothing with double seasonality A description of the identification of exponential smoothing models for double seasonality can be found

Towever, the MAPE corresponding to the 24-step-ahead forecast of the out-of-sample period hardly outperformed the ARIMA model and it turned out equal than the exponential smoothing's one: 8.53%. The cross correlation function (CCF) of the differenced and log-transformed wind power production

Taking this into account, a periodic dynamic regression model whose parameters switch according to the

Fig. 4. Bidding curves for 18 and 21 March 2003 (Sunday and Wednesday, respectively). Note the intersection point of supply and demand curves. switching model present two fixed unit roots: one regular and another corresponding to the within-week

Fig. 6. Analysis of the input/output sensitivities for the neural network. ‘ig.6. The upper picture illustrates the 95%-percentiles of the sensitivity absolute values normalize and fj are all hyperbolic tangent functions. The analysis of the input/output sensitivities (Oy/Ox) is shown in

Fig. 5. Price (solid line) and demand (dash line, in different scales) in some days of August. Note the spikes in the price when demand is high, and saturations when demand is low. of demand hour. Model 5 uses 12 periods, as a combination of model 1 with model 4. None of these models

Fig. 8. Cumulative MAPE of all tested methods along the whole out-of-sample period. The behavior of prices in these special days is similar to Saturdays and Sundays due to the reduction in

Fig. 9. Actual and predicted prices of the Holt- Winters’, ARIMA and DR with load methods during two different weeks with holidays (12 October on the left and 25" December on the right). the electricity load as an explanatory variable permits to take into account these calendar effects (see Fig.

DOUBLE SEASONAL EXPONENTIAL SMOOTHING MODELS FITTED Fig. 2 shows the weekly and daily seasonal components for two weeks within the fitting period. The daily

MEAN OF SQUARE ERRORS OF THE DY NAMIC REGRESSION MODEL BY DAY OF THE WEEK TABLE II

SUMMARY OF THE FITTED PERIODIC MODELS more significant increase in price for the same increase in the effective demand. is displaced to the left in the figure, and enters a segment of higher slope. A higher slope is equivalent to a

Regarding the univariate tested methods, exponential smoothing performed slightly better than ARIMA,

SUMMARY OF THE DIEBOLD- MARIANO STATISTICAL TEST

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Electrothermal Coordination Part I: Theory and Implementation Schemes

by Hadi Banakar

2005, IEEE Transactions on Power Systems

The concept of electrothermal coordination (ETC) in power system operation is proposed. ETC exploits thermal inertia to coordinate line temperature dynamics with existing system controls, thus increasing power transfer capability and... more

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Dynamic sizing of energy storage for hedging wind power forecast uncertainty

by G. Papaefthymiou

2009

In market conditions where program responsible parties are penalized for deviations from proposed bids, energy storage can be used for compensating the energy imbalances induced by limited predictability of wind power. The energy storage... more

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Auction design in day-ahead electricity markets

by Javier Contreras

2001, IEEE Transactions on Power Systems

Competition in day-ahead electricity markets has been established through auctions where generators and loads bid prices and quantities. Different approaches have been discussed regarding the market auction design. Multi-round auctions,... more

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Optimizing post-combustion CO2 capture in response to volatile electricity prices

by Stuart Cohen

2012, International Journal of Greenhouse Gas Control

Flexibly operating CO 2 capture at power plants allows a temporary increase in electrical output, which could help maintain grid reliability, meet peak demand, or improve profitability when electricity prices are high. This article... more

Fig. 1. Simultaneously reducing steam and rich solvent flow to the stripper allows increased power output but increases CO2 emissions. 1.3. Scope This work considers two concepts for flexibly operating an amine scrubbing system. One concept reduces the energy require- ments of CO stripping and compression while allowing CO2 removal rate to fall. Fig. 1 uses a simplified process diagram to dis- play one way to operate in this manner by reducing steam and rich solvent flow rates to the stripper equally and simultaneously

Fig.2. Including solvent storage incurs significant capital costs but allows continued high COz removal at partial and zero load capture.

Fig. 3. The 1st order model calculates prices that strictly follow trends in demand which does not reproduce historical ERCOT electricity price spikes.

Fig. 4. By incorporating electricity price volatility, the optimization model calcu- lates a much greater benefit of CO2 capture flexibility. To observe the economic benefit from flexibility with and with- out a model that incorporates electricity price volatility, Fig. 4 plots the annual operating profit benefit provided by flexible oper- ation at each CO, price, and the profit benefit is normalized by the electrical output capacity before COz capture energy require- ments (500 MW in the profit maximization model, 492 MW in the first-order dispatch model). The normalized profit benefit is plot- ted for both flexible configurations in the optimization model and the venting-only configuration in the first-order dispatch model. Flexible capture facilities will operate COz capture nearly contin- uously at 0% load at low CO, prices and 100% load at high CO, prices, and the primary opportunity to operate flexibly occurs at moderate CO, prices. At low CO; prices in the profit maximization model, profits with flexible capture are lower than those with- out CO capture because the model assesses a fixed cost for CO capture even though systems are not utilized. However, by using realistic electricity price volatility, the profit maximization model finds a much greater economic benefit for flexibility at moderate COp prices than the first-order dispatch model while accounting for system operating constraints in much greater detail. The first-order model finds a small range of CO2 prices where venting CO at high

Fig.5. Three CO2 price paths from the EIA analysis of the APA2010 provide scenarios to compare each CO? capture configuration (USEIA, 2010). The limited formulation described in Section 3 and Appendix Cis again used to compare the operating behavior, CO2 emissions, and net present value of each of the four plant configurations over three projected 20-year CO, price paths. The three CO, price paths are chosen from the EIA analysis of the proposed American Power Act of 2010 (APA2010), which proposed to create a cap-and-trade pro- gram for CO and six other greenhouse gases (GHGs) (USEIA, 2010). These price paths are projections calculated from the EIA National Energy Modeling System under several variations of APA2010 pol- icy. Future CO2 prices in the United States are highly uncertain, and a CO emissions reduction policy could take many other forms than cap-and-trade. Nevertheless, any policy will produce an effec- tive CO2 price, so facility behavior under low, intermediate, and high CO2 price conditions is examined in this work using the last 20 years of CO2-equivalent allowance price paths for the “Basic,” “No International,” and “Limited Tech., No International” scenarios (Fig. 5). “Basic” is the nominal scenario, “No International” implies more expensive GHG mitigation because international offsets can- not be used to meet GHG limitations, and “Limited Tech.” assumes key GHG abatement technologies do not develop more quickly in response to GHG limitations (USEIA, 2010). The assumptions of

Fig. 6. The 2008 ERCOT electricity price—duration curve is shown with two adjustec curves for $50/tCO2 and $100/tCO2.

Fig. 7. CO2 emissions decrease over time due to increased CO2 capture utilization and decreased operation without CO. capture.

Fig. 8. Average CO emissions rate demonstrates the transition of flexible capture systems from near-continuous 0% load operation to continuous 100% load operation.

Fig. 9. CO2 capture flexibility allows greatest operating profits, especially at the intermediate CO2 prices experienced in 2025-2029 under the Basic COz price path. The No International scenario has much higher CO; prices than the Basic scenario, so profits without COz capture are much lower, and there is less benefit to CO capture flexibility. CO2 capture is critical to maintaining operating profits with these CO, price conditions, and while the benefit from flexibility is small, it is main- tained throughout the time horizon. The temporal trends in the No International scenario are exaggerated in the Limited Tech., No

Fig. 10. High CO2 prices are required for NPV to be greatest with CO2 capture, but flexibility provides at least some benefit relative to an inflexible CO capture config- uration for all three scenarios that were considered. Thus, flexibility should improve the economic viability of CO2 capture.

Fig. C.3. At $50/tCO2, higher ramping costs lead to a modest decrease in annual operating profits. formulation results. Fig. C.3 displays the annual operating profits fo! each CO2 capture configuration versus COz capture ramping cost and Fig. C.4 shows corresponding computation time. Flexible CO; capture is utilized often at $50/tCO2, so ramping the CO2 capture system is often desirable. Profits are reduced by 1% or more above a ramping cost of $5/% with solvent storage, $7.5/% with venting- only flexible capture, and $10/% for inflexible capture. Computatior time remains low (1-2 min) for the inflexible and venting-only con- figurations. Ramping costs only substantially increase computatior time for the solvent storage scenario, for which achieving sufficient

Fig. C.2. Including a ramping cost of $10 for each 1% change in absorption and stripping load damps rapid load changes.

Fig. C.1. Absorber and stripper load can be erratic with no ramping cost (5%/min ramp rate).

Fig. C.6. Including base plant minimum up and down time constraints in the mode adds significant computation time. convergence required raising the default model computation time limit from 10,000s to 30,000s at low ramp costs and 20,000s at high ramp costs. However, ramp costs do not increase computa- tion time for the solvent storage configuration at the $10/% implied by Husebye (Husebye et al., 2010). The effect of ramping costs on annual operating profits exceeds the 1% threshold discussed above, and increased computation time is only an issue for certain cases with solvent storage, so ramping costs are included in the limited formulation.

Fig.C.5. Base plant minimum up and down time has little effect on operating profits for facilities with CO that primarily provide base load. Fig. C.4. Reasonable CO capture ramping costs do not excessively increase compu tation time.

The following are the key input parameters used in the single plant profit maximiza- tion model. Table 1

Results (STAR) Graduate Fellowship Program and the Luminant Carbon Management Program. However, the views expressed within this publication might not reflect the views of the sponsoring organizations.

Analogous CO capture system constraints are produced by making the following substitutions in Eqs. (B.17)-(B.21). B.5. CO> capture system constraints Table B.1

Parameters for several constraints and costs were varied to examine their effect on computation time and deviation from the general formulation solution. Table C.1

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Application of fuzzy Q-learning for electricity market modeling by considering renewable power penetration

by Mohammad Reza Salehizadeh and

By increasing renewable resource penetration, the need for developing fast and reliable market modeling approaches in the presence of these resources has gained greater attention. In this paper, fuzzy Q-learning approach is proposed for... more

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Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)

by N. Bigdeli

2011, Energy

In this paper, the data analysis and short term load forecasting (STLF) in Iran electricity market has been considered. The proposed method is an improved singular spectral analysis (SSA) method. SSA decomposes a time series into its... more

The expression in Eq. (4) corresponds to averaging the elements along diagonals i+j=k+2. This diagonal averaging, applied to a resultant matrixx;,, produces a time series Y, of length T and thus the initial series Yr is decomposed into the sum of M series. Calling the decomposed series as Y, it will be derived as: Where, the trajectory matrix X is represented as a sum of M resultant matrices. Next, diagonal averaging transfers each matrix X;,,n = 1,...,M into a time series. Let X be a (L x K) matrix with elements xj. Make L* = min(L, K), KY = max(L,K). Lets define xij = xj, if L<K; and xj =x;, otherwise. Diagonal averaging transfers matrix X to a series go,...,g7_1 following the formula:

Fig. 1. (a) The experimental hourly required load (RL) of Iran electricity network for 844 days, (b) some parts of Fig. 1(a) magnified. The experimental data is the RL time series of Iran electricity market in MWh for 844 days from 24 September 2005 up to 15 Dec.

Fig. 2. Principal components related to the first 36 Eigen triples

Fig. 3. Logarithms of the 48 Eigen values. In SSA analysis, there is some extra information, which helps us to identify of the useful Eigen triples of the SVD of the trajectory

Fig. 4. Periodograms of the paired Eigen triples for RL series. Usually every harmonic component with a different frequency produces two Eigen triples with close singular values. These Eigen pairs will be clearer if T, L and K are sufficiently large. Another useful insight is provided by checking breaks in the Eigen value spectra. As arule, a pure noise series produces a slowly decreasing sequence of singular values. Therefore, explicit plateau in the Eigen value spectra prompt the ordinal numbers of the paired Eigen triples [26]. Fig. 3 depicts the plot of the logarithms of the 48 singular values for the RL series where the values of the singular value logarithms versus the Eigen value numbers are listed in Table 1 for more

Fig. 5. The reconstructed RL (dash) in comparison with the actual data (Line) for different time intervals. The periodogram analysis of the original series and Eigen vectors tells one which frequency must be considered. If the periodograms of the Eigen vector have sharp spark(s) around some frequencies, then the corresponding Eigen triples must be regarded as those related to the signal component [29]. Fig. 4 depicts the periodogram of the paired Eigen triples (2, 3), (4, 5), (6, 7), (8, 9), (10, 11), (12, 13), (14, 15), (18, 19), and (24, 25) of the RL series. From Fig. 4, all the above-mentioned Eigen triples should be regarded as the selected Eigen triples in the grouping step in conjunction with another Eigen triple (the first Eigen triple) that we need to reconstruct the series. In SSA forecasting, the model is expressed by a Linear Recurrent Formula (LRF). This LRF applied to the last L— 1 terms of the initial

Where, in Eq. (10), 0 < a, 6 < 1,a+ 6 = 1 are constants which should be chosen and Yy7c1,1)is the first element of Yrc which has been subtracted from it to avoid continuity violation. A proper choice for

Fig. 7. Comparison of forecasted RL with its actual value for: (a) the February week, (b) The May week, (c) The August week, and (d) The November Week.

Logarithms of the 48 Eigen values versus the Eigen value number. Table 1

DME, and DPE of four weeks for the forecasted RL time series. Table 2

WME and WPE of four weeks for the forecasted RL time series. References

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Competition, Risk and Learning in Electricity Markets: An Agent-Based Simulation Study

by Danial Esmaeili Aliabadi

2017

This paper studies the effects of learning and risk aversion on generation company (GenCo) bidding behavior in an oligopolistic electricity market. To this end, a flexible agent-based simulation model is developed in which GenCo agents... more

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Impacts of the European emission trade system on Finnish wholesale electricity prices

by Ville Mälkönen and

2006, Discussion Papers

nid.), ISBN 951-561-666-2 (PDF).

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Electricity market equilibrium based on conjectural variations

by Cristian Diaz

2010, Electric Power Systems Research

Many of the models used for the representation of the generation companies’ behavior in oligopolistic electricity markets are based on conjectural variation equilibria, which are a generalization of the classic Cournot approach. However,... more

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Equilibrium prices supported by dual price functions in markets with non-convexities

by Kurt Jörnsten

2008, European Journal of Operational Research

The issue of finding market clearing prices in markets with non-convexities has had a renewed interest due to the deregulation of the electricity sector. In the day-ahead electricity market, equilibrium prices are calculated based on bids... more

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Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

by Ashkan Rahimi-Kian

Applied Soft Computing

This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for... more

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Vertical Integration and the Restructuring of the U.S. Electricity Industry

by Robert Michaels

2004, SSRN Electronic Journal

Debates on restructuring of the U.S. electricity industry are often about the degree to which market relationships should replace transactions that formerly took place within regulated, vertically integrated utilities. Markets for the... more

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Distributed generation and demand response dispatch for a virtual power player energy and reserve provision

by T. Soares

2014, Renewable Energy

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the... more

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The Merit Order Effect of Wind Generation on the Irish Electricity Market

by Amy O'Mahoney

This paper considers the cost savings arising from wind generation through the merit order effect in gross pool electricity markets, using the Irish electricity market as a case study. The Irish electricity market makes for a good... more

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Rents in the European power sector due to carbon trading

by Michel Cruciani and

2010, Energy Policy

08 Autom ne L a b o r a t o i r e d ' E c o n o m i e d e D a u p h i n e ( L E D a ) WP n°4 09 Document de travail

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Lessons from deregulation: Understanding electricity markets in South America

by Isaac Dyner

2006, Utilities Policy

South America has been the most progressive region in the developing world in terms of deregulating the electricity industry. In this paper we compare the evolution of deregulation, from initiation to the current state, in four South... more

Fig. 2. Monomial average annual price in Argentina. Source: ENRE (2002).

Fig. 1. Evolution of the Argentinean electricity market: installed capacity, peak demand. Source: CAMMESA (2003).

Fig. 3. Electricity prices in Brazil, monthly overall average from the four sub- markets (own estimation, data from MAE, 2003a,b).

Fig. 4. Quality indicators’ development: DEC (duration of interruption per consumption unit) and FEC (frequency of interruption per consumption unit). Source: MAE (2003a,b). The non-technical losses in Brazil have not changed signif- icantly after deregulation. The non-technical losses have al- ways been between 9% and 10% (MAE, 2003a,b). The quality of the service in Brazil, measured by the two indicators DEC and FEC, is shown in Fig. 4. The DEC (Duragao Equiv- alente de Interrupgao por Unidade Consumidora) represents average duration of interruptions in a year, while the FEC

Fig. 5. Time series of the total generation and total capacity in the SIC. Source: CDEC-SIC (2002).

Fig. 6. Spot price (monthly average, December 2002) and level of ‘Las Lajas” lake. Note: the spot price in the Chilean electricity market does not include the aggregated value of distribution but it does include the transmission cost. Source: adapted from CDEC-SIC (2002). That the system in Chile is dominated by a very large com- pany leads to a potential market power problem (ENGESA with 32% of the installed capacity in the SIC). However, the

Fig. 7. Installed capacity and monthly maximum power demand in Colombia. Source: calculated with data from ISA (2002).

Fig. 8. The evolution of pool and contract electricity prices in Colombia from 1995 to 2002. Source: ISA (2002).

Fig. 9. Monthly spot price evolution of the main electricity markets models’ in South America, standardized in US dollars per MWh (US$/MWh).

Fig. 10. Yearly volatility of the monthly spot prices of the main electricity markets models in South America, estimated from prices in US dollars per MWh.

Comparison of macro economic indicators and electricity industries in the four selected countries World Bank (2003). Only the MEM, major subsystem with more than the 90% of the total electricity system in Argentine (Energia, 2002). Brazilian Energy Balance (BEB) 2002, MME. Only the SIC, major subsystem with more than 90% of the total demand in Chile. The total system in Chile has an installed capacity of 10,459 MW in December 2002 (CDEC-SIC, 2003; ISA, 2003: Espinasa, 2001).

* Source: Bacon and Besant-Jones (2001). Summary of development in four South American countries Table 2

Qualitative cross comparison of the electricity markets’ performance in Argen- tina, Brazil, Chile, and Colombia

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Reprint of “The future of hydropower in Europe: Interconnecting climate, markets and policies”

by Ludovic Gaudard

2014, Environmental Science & Policy

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Allocation and leakage in regional cap-and-trade markets for CO2

by Yihsu Chen

2012, Resource and Energy Economics

The allocation or assignment of emissions allowances is among the most contentious elements of the design of emissions trading systems. Policy-makers usually try to satisfy a range of goals through the allocation process, including easing... more

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A system dynamics analysis of the Nordic electricity market: The transition from fossil fuelled toward a renewable supply within a liberalised electricity market

by Klaus Vogstad

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The determinants of electricity theft: An empirical analysis of Indian states

by Vasundhara Gaur

More than 20% of the electricity generated in India is lost to rampant thefts. Drawing data from 28 states of India over a time span of five years (2005–2009), this paper examines the role played by socio-economic and governance factors... more

addition to socio-economic factors (Min and Golden, 2014; Smith 2004; Steadman, 2011). In an attempt to find a relation between populism and power theft, Min and Golden (2014) undertook a study in the state of Uttar Pradesh in India and found a concrete relation between electricity theft and electoral cycles by demon- strating how theft rates increased just prior to a new election term. down to optimal levels. However, it is the component of com- mercial losses (including electricity theft through pilferage and other fraudulent practices) that constitutes a major part of the total T&D losses and which goes unaccounted for (Smith, 2004).

Fig. 2. State-wise profits and T&D losses of utilities for 2009 (All India Electricity Statistics, 2011; Report on the Performance of State Power Utilities for the years 2009-10 ti 2011-12, Power Finance Corporation (PFC)).

Fig. 4. Socio-economic indicators for Indian states for 2009 (All India Electricity Statistics, 2011; Handbook of Statistics on Indian Economy, Reserve Bank of Indic

Regression results for Feasible Generalized Least Squares Model*. 34 observations as against 140 due to missing data. * Standard errors in parenthesis. "P<0.10. ™ P<0.05. “ P<0.01.

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An analysis of capacity and price trajectories for the Ontario electricity market using dynamic Nash equilibrium under uncertainty

by Suvrajeet Sen

2008, Energy Economics

This paper studies investments in the Ontario Electricity Market which is currently being restructured. Our methodology is based on the concept of S-adapted open-loop Nash equilibrium. We examine the evolution of capital investments and... more

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The value of dispatchability of CSP plants in the electricity systems of Morocco and Algeria

by Amine Boudghene Stambouli

2012, Energy Policy

c Market model to optimize CSP plant configuration in North African power systems. c Value of storage-based CSP plants compared to non-dispatchable configurations: 28-55 h/MWh. c Assessment of Morocco's and Algeria's renewable electricity... more

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Market structure in the US electricity industry: A long-term perspective

by Peter Klein

2005, Energy Economics

We estimate changes in market structure in the US electric power industry during the last three decades using two independent approaches. First, we estimate an industry-wide conjectural-variations parameter. We find that industry... more

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Market efficiency and risk premia in short-term forward prices

by Erik Haugom

2012, Energy Economics

JEL classification: G14 G13 Q47 L94

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Evaluating risk-constrained bidding strategies in adjustment spot markets for wind power producers

by JULIO USAOLA GARCIA

2012

Participation of wind energy in the day-ahead electricity market implies large deviations from the initial schedule, which leads to costs for the wind farm owner. By means of short-term wind power prediction programs, the contracted... more

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Spot Market Mechanism Design and Competitivity Issues in Electric Power

by Stephen Rassenti

1998

Continuing previous research on market power issues in electricity markets [2], we report new experiments which compare the sealed bid offer (SBO) market mechanism, studied in [2], with a uniform price double auction mechanism (UPDA) that... more

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The U.S. electricity market twenty years after restructuring: A review experience in the state of Delaware

by Wei-Ming Chen

2019, Utilities Policy

Electricity industry restructuring in the United States, which began in the 1990s, was aimed at enhancing market competitiveness and lead to efficient resource allocation and cost reduction. The state of Delaware was one of the earliest... more

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Electricity Markets

Key research themes

1. How do distributed energy resources (DERs) and demand response programs transform electricity market structures and operations?

2. What factors contribute to price dispersion and instability in liberalized electricity markets, and how can market power affect price formation?

3. How can electricity market models be improved to realistically represent price variability and support integration of renewables and new technologies?

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