Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost
Abstract
:1. Introduction
- Objective 1: Analysis, systematisation and selection of optimal centralised microgrid management in islanded operation. This paper presents a comprehensive analysis of existing microgrid management models, focusing on their optimisation methods. Different approaches, including classical methods, metaheuristic techniques and artificial intelligence-based management, are analysed. Metaheuristic Particle Swarm Optimisation (PSO) is used due to its adaptability to problems with different types of constraints and objective functions and its iterative process of searching the solution space, improving the results through multiple iterations [34,35].
- Objective 2: Simulation model of centralised microgrid management considering microgrid components. In this paper, a detailed simulation model for centralised microgrid management is developed considering all relevant components such as photovoltaic panels, battery storage systems, diesel generators and bidirectional converters. This model was created using MATLAB R2022a Simulink and the Simscape Power Systems Toolbox and provides a robust environment for analysing and optimising the performance of microgrids under different conditions and configurations.
- Objective 3: Evaluation of the microgrid simulation model. The effectiveness of the proposed simulation model is evaluated through extensive simulations. Comparative analyses will be conducted to measure the performance of different management strategies, focusing on cost minimisation, reliability and resource utilisation. The results highlight the benefits of integrating advanced optimisation techniques such as PSO for optimal microgrid management.
2. Methodology and Mathematical Modelling
2.1. Selection of Optimal Management
- Preparation of the microgrid model: A microgrid model with discrete data is created to simulate the real operating conditions. MATLAB R2022a Simulink with the Simscape Power Systems Toolbox, which enables the modelling and analysis of energy systems, is used for the simulation. The model includes PV modules, a battery, a diesel generator, a bidirectional converter and loads. Parameters and constraints are also defined for each element of the MG.
- Implementation of the PSO algorithm (Figure 3): The PSO algorithm is used to optimise the management of the MG. This process includes initialising a population of particles, evaluating the objective function, updating the velocity and position of the particles, checking convergence and selecting the optimal management model.
- Simulation of the proposed management algorithms: The proposed management algorithms are simulated in MATLAB R2022a Simulink using the Simscape Power Systems Toolbox. This setup enables the modelling and analysis of power systems. Parallel processing is used to speed up the execution of the algorithms and achieve efficient results.
- Analysis and comparison of the results: The results of the two algorithms for the management of the microgrid in islanded mode are analysed and compared. The production costs, including the fuel costs for the diesel generator, the maintenance costs and the costs of replacing batteries and inverters, are taken into account to minimise the objective function (UTP). The effects of various parameters on the optimisation process and the quality of the solution are also analysed.
2.2. Economic Indicators for Microgrid Optimisation
- o
- All system costs over the lifetime minus all revenues.
- o
- The value of the costs is reduced to present value by discounting.
- o
- Included costs: capital costs, replacement of system components, operation and maintenance costs, fuel, electricity purchased from the grid.
- o
- Revenues include sale of electricity to the grid, residual value of the system at the end of the project.
2.3. Defining the Input Data for the Microgrid Model
2.4. Location and Meteorological Data
2.5. Electricity Consumption Requirements at the Site
2.6. Selection of Optimal Microgrid Management in Island Operation
3. Evaluation Results and a Comparative Analysis
4. Discussion
- Objective 1: Analysis, systematisation and selection of optimal centralised microgrid management in islanded operation. The analysis and systematisation of optimisation methods, especially the PSO method, provided a comprehensive understanding of their performance and led to the selection of the M70 algorithm as the optimal model for centralised microgrid management in islanded operation, based on the minimisation of production costs.
- Objective 2: Simulation model for centralised microgrid management considering microgrid components. The development and implementation of detailed simulation models in MATLAB R2022a Simulink for both algorithms enabled an accurate evaluation of their performance under different conditions and revealed the strengths and weaknesses of each approach.
- Objective 3: Evaluation of the simulation model for the microgrid. The comprehensive evaluation of the simulation results highlighted the economic and operational benefits of the M70 algorithm and confirmed its superiority over the P algorithm in terms of cost efficiency and system reliability.
5. Conclusions
- The M70 algorithm achieved a total project cost (UTP) of EUR 2,312,823, which is significantly lower than the EUR 2,666,491 of the P model.
- The M70 model had lower maintenance and fuel costs due to the efficient operation of the diesel generator and the optimised balance of PV and battery capacities.
- The use of the PSO method to dynamically adjust the chrLim parameter in the M70 model proved to be highly effective in minimising costs and improving the overall efficiency of the system.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Year | Proposed Optimisation Method | Advantages | Disadvantages |
---|---|---|---|---|
[8] | 2018 | PSO | Cost-effective, shorter calculation time compared to other methods | Requires detailed meteorological and load data |
[16] | 2018 | Dynamic programming | Achieves global optimal solutions quickly, accurate modelling, handles switching costs effectively | Computer complexity and high resource requirements for large problems |
[42] | 2018 | Multi-Objective Particle Swarm Optimisation (MOPSO) | Efficient handling of multiple objectives using Pareto dominance, maintains diversity through a global repository. | Complexity in implementation, requires careful tuning of parameters for effective performance |
[43] | 2018 | Various methods, including PSO, GA, MILP and rule-based approaches | A wide range of methods ensures flexibility and robustness in optimisation, suitable for various microgrid scenarios | High computational complexity, need for extensive data and parameter tuning |
[33] | 2019 | PSO | High reliability, optimised cost of energy (COE) and total net present cost (TNPC), efficient management of constraints | Requires considerable computing resources, the quality of the solution depends on the parameters and initial conditions |
[39] | 2019 | Deep Reinforcement Learning (DRL) | Real-time optimisation, processing of high-dimensional data, adaptability to changing environments | High computing requirements, complexity in implementation and tuning |
[44] | 2020 | Simulation using HOMER Pro 3.14.5 software | Significant reduction in annual electricity costs (83.94%), high proportion of renewable energy (67.1%), environmentally friendly design | Without diesel generators, increasing dependence on photovoltaics and grid stability |
[45] | 2020 | Simulation using HOMER Pro 3.14.5 software | Optimum configuration with low net present cost (NPC) and levelised cost of energy (LCOE) identified | High initial investment, complexity of system integration |
[46] | 2020 | PSO | High efficiency in exploring the search space, good compromise between different goals | Risk of premature convergence to local optimum |
[47] | 2020 | Genetic Algorithm (GA) for optimisation using MATLAB R2022a | Achieves minimum total annual costs (TAC) and a high proportion of renewable energy (98.72%) | High computational requirements, complex implementation |
[6] | 2021 | Robust Optimisation (RO) using Bi-level Max-Min Optimisation | Takes into account uncertainties in renewable energy production, demand and market prices, leading to more reliable and robust solutions | Computationally intensive, requires iterative process and complex implementation |
[34] | 2021 | Three types of PSO (Inertia Weight PSO, Constriction PSO, Momentum PSO) | High efficiency in parameter extraction, improved accuracy, fast convergence | Potential risk of premature convergence, computationally intensive |
[36] | 2021 | Combined use of Central Moving Average (CMA) approach | Reduces battery size by 25%, achieves a better grid current profile, minimises current peaks and fluctuations | Computer complexity, requires accurate prediction of performance profiles |
[1] | 2022 | Hybrid Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) | Minimises the total cost of ownership of microgrids, manages worst-case uncertainties, improves reliability and robustness | High computational complexity, requires extensive computing resources |
[35] | 2023 | Multi-Objective Particle Swarm Optimisation (MOPSO) | Improves solution diversity, ensures a broad spectrum of non-dominant solutions, mitigates premature convergence | Complex implementation, requires high computing resources |
[48] | 2023 | Cost-effective Multi-Verse Optimisation Algorithm (CMVO) | Efficient energy planning, lower generation costs, better convergence speed | Complexity in implementation, requires careful parameter tuning |
[41] | 2024 | PSO | Flexibility, fast convergence time, high performance | High computing requirements, complexity of implementation |
2024 | This paper—PSO | Adaptability to various constraints and objectives, improved cost efficiency and system reliability | Requires detailed tuning of the parameters and can be computationally intensive for extensive applications |
Optimal Values | 1. PSO Algorithm P | 2. PSO Algorithm M70 | ||
---|---|---|---|---|
PPVmin–PPVmax | KBTmin–KBTmax | PPVmin–PPVmax | KBTmin–KBTmax | |
1. iteration | 100–400 | 50–500 | 100–400 | 50–250 |
2. iteration | 240–440 | 275–500 | 90–290 | 50–150 |
3. iteration | 290–390 | 388–500 | 140–240 | 75–125 |
PPV i KBT | 330 kW | 419 kWh | 190 kW | 100 kWh |
UTP | EUR 2,666,491 | EUR 2,312,823 | ||
COE | EUR 0.397 | EUR 0.343 | ||
NPC | EUR 2,065,129 | EUR 1,690,412 | ||
LCOE | EUR 0.307 | EUR 0.251 |
Model | PV (kW) | Converter (kW) | Battery (kWh) | DG (kW) | PV Energy (kWh) | DG Energy (kWh) | Average Power DG (kW) | Fuel (L) 0.335 L/kWh | Consumption (kWh) |
---|---|---|---|---|---|---|---|---|---|
P | 330 | 313.5 | 419 | 100 | 222,189 | 66,882 | 30 | 28,370 | 269,431 |
M70 | 190 | 180.5 | 100 | 100 | 143,454 | 138,728 | 100 | 38,671 | 269,431 |
Economic Indicators | Management Algorithm | ||
---|---|---|---|
Optimal Point of Algorithm P | M70 | P | |
Initial Investment | EUR 751,411 | EUR 751,411 | |
Annual Maintenance and Fuel Costs | EUR 52,532 | EUR 58,870 | |
Total Project Cost (UTP) | EUR 2,508,051 | EUR 2,666,491 | |
Cost of Energy per kWh (COE) | EUR 0.372 | EUR 0.396 | |
Average Annual Cost | EUR 100,322 | EUR 106,660 | |
Net Present Cost (NPC) | EUR 1,950,789 | EUR 2,058,490 | |
Levelised Cost of Energy per kWh (LCOE) | EUR 0.290 | EUR 0.306 |
Economic Indicators | Management Algorithm | ||
---|---|---|---|
Optimal Point of Algorithm M70 | M70 | P | |
Initial Investment | EUR 363,022 | EUR 363,022 | |
Annual Maintenance and Fuel Costs | EUR 72,641 | EUR 103,287 | |
Total Project Cost (UTP) | EUR 2,312,823 | EUR 3,078,970 | |
Cost of Energy per kWh (COE) | EUR 0.343 | EUR 0.457 | |
Average Annual Cost | EUR 92,513 | EUR 123,159 | |
Net Present Cost (NPC) | EUR 1,690,412 | EUR 2,211,205 | |
Levelised Cost of Energy per kWh (LCOE) | EUR 0.251 | EUR 0.328 |
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Žigman, D.; Tvorić, S.; Lonić, M. Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost. Energies 2024, 17, 3901. https://doi.org/10.3390/en17163901
Žigman D, Tvorić S, Lonić M. Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost. Energies. 2024; 17(16):3901. https://doi.org/10.3390/en17163901
Chicago/Turabian StyleŽigman, Dubravko, Stjepan Tvorić, and Manuel Lonić. 2024. "Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost" Energies 17, no. 16: 3901. https://doi.org/10.3390/en17163901