A Multi Agent-Based Optimisation Model for the Distribution Planning and Control of Energy-Based Intermittent Renewable Sources
International Journal of Energy Research , 2021
Recent work on the management of renewable energy sources focuses on developing innovative tools ... more Recent work on the management of renewable energy sources focuses on developing innovative tools and techniques to control the different behaviours of energy generation systems (e.g. the intermittent production of wind power). These tools contribute to achieving a proper balance between energy supply and consumer demand and guarantee a sustainable energy level in storage devices that can face potential generation shortages. In contrast, previous studies mainly focused on stimulating consumer response to market prices so as to rationalise consumption, especially during peak periods. This study investigates the impact of supplier decisions about using different renewable energy sources on the distribution planning and control of energy for the best consumer demand satisfaction and achieving a sustainable energy level in different located storage devices. A Multi Agent-Based Heuristic Optimisation model is developed to deliver this aim. The heuristic optimisation part of this model is proposed to optimise the energy level across differently located storage devices. It also guarantees the best energy exchange between regions at the strategic planning level. A sensitivity analysis study is conducted to verify the behaviour of the proposed model towards various demand levels, followed by a comparison study to justify the proposed agent-based heuristic model’s superiority. The results highlight the impact of using intermittent renewable sources including solar, wind, hydro and storage devices on energy production, control and distribution. In addition, a sustainable level of storage in devices placed at different locations is achieved by optimising the energy storage operation. This sustainable storage aids any insufficient energy generated from intermittent sources to satisfy consumer requirements. The best energy exchange between regions is also presented.
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Papers by Ammar Al-Bazi
uncertainties associated with storing and retrieving containers from the yard. These
associated uncertainties occur because the arrival of a truck to pick up the container
is random, so the departure time of the container is unknown. The problem investigated in this paper emerges when newly arrived containers of different sizes, types and
weights require storage operation in the same yard where other containers have already
been stored. This situation becomes more challenging when the time of departure of
existing container is not known. This study develops a new Fuzzy Knowledge-Based
optimisation system named ‘FKB GA’ for optimal storage and retrieval of containers
in a yard that contains long stay pre-existing containers. The containers’ duration of
stay factor is considered along with two other factors such as the similarity (containers
with same customer) and the quantity of containers per stack. A new Multi-Layered
Genetic Algorithm module is proposed which identifies the optimal fuzzy rules required
for each set of fired rules to achieve a minimum number of container re-handlings when
selecting a stack. An industrial case study is used to demonstrate the applicability and
practicability of the developed system.
In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.
considered by yard operators to be a very challenging task
due to the many uncertainties inhe rent in such operations.
The storage of the containers is one of those operations that
require proper managem ent for the efficient utilisation of
the yard, requiring rapid retrieval time and a minimum
number of re-handlings. The main challenge is when
containers of a different size, type, or weight need to be
stored in a yard that holds a number of pre-existing con-
tainers. This challenge becomes even more complex when
the date and time for the departure of the containers are
unknown, as is the case when the container is collected by
a third-party logist ics company without any prior notice
being given. The aim of this study is to develop a new
system for the management of container yard operations
that takes into consideration a number of factors and con-
straints that occur in a real-life situation. One of these
factors is the duration of stay for the topmost containers of
each stack, when the containers are stored. Because the
duration of stay for containers in a yard varies dynamically
over time, an ‘ON/OFF’ strategy is proposed to activate/
deactivate the duration of stay factor constraint if the
length of stay for these containers varies significantly over
time. A number of tools and techniques are utilised for
developing the proposed syst em including: discrete event
simulation for the modelling of container storage and
retrieval operations, a fuzzy know ledge-based model for
the stack allocation of containers, and a heuristic algorithm
called ‘neighbourhood’ for the container retrieval opera-
tion. Results show that by adopting the proposed ‘ON/OFF’
strategy, 5% of the number of re-handlings, 2.5% of the
total retrieval time, 6.6% of the total re-handling time and
42% of the average waiting time per truck are reduced.