Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty

Goh, Hui Hwang; Shi, Shuaiwei; Li, Xue; Zhang, Dongdong; Dai, Wei; Liu, Hui; Wong, Shen Yuong; Kurniawan, Tonni Agustiono; Goh, Kai Chen; Cham, Chin Leei

doi:10.1016/j.apenergy.2022.120094

Cited by 15 publications

(4 citation statements)

References 46 publications

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“…This adjustment increased the cover rate and the overall front of the non-dominated solutions. In view of the shortcomings of PSO such as lack of randomness in particle position changes and numerous parameters, the theory of quantum mechanics is combined with PSO to solve the non-linear and non-convex optimization problem considering high uncertainty from the power output of PV and wind turbine in microgrid [235]. The optimal scheduling method for power resources in microgrid considering the uncertainty of renewable energy output is vital especially when the generation of actual maximum power from wind and PV is less than the power arranged in the scheduling plan, increasing operating costs and frequency fluctuation of the microgrid which may harm the system [236].…”

Section: B Uncertainty In Optimizationmentioning

confidence: 99%

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

et al. 2023

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The increase in energy consumption, environmental pollution issues, and low-carbon agenda has grown the research area of demand side management (DSM). DSM programs provide feasible solutions and significantly enhance the efficiency and sustainability of electrical distribution systems. This paper classifies and discusses the broad definition of DSM based on the comprehensive literature study considering demand response and energy efficiency. The implementation of Artificial Intelligence algorithms in DSM applications has been employed in many studies to help researchers make optimal decisions and achieve predictions by analyzing the massive amount of historical data. Owing to its simplicity and consistent performance in fast convergence time, Particle Swarm Optimization (PSO) is widely used as a part of the swarm AI algorithm and has become a prominent technique in the optimization process to exploit the full benefit of the demand-side program. The variants of PSO have been developed to overcome the limitations of the original PSO and solve the high complexity and uncertainty in the DSM optimization process. The proposed PSO-based algorithm can optimize consumers' consumption curves, reducing the peak demand and hence minimizing the electricity cost when integrated with the DR programs or EE measures. The research works of the PSO algorithm in DSM have seen an increasing trend in the past decade. Therefore, this paper reviewed the application of the PSO-based algorithm in DSM fields with some constraints and discussed the challenges from the previous work. The potential for new opportunities is identified so that PSO methods can be developed for future research.INDEX TERMS Demand side management (DSM), demand response (DR), energy efficiency (EE), metaheuristic algorithms, particle swarm optimization (PSO), swarm intelligence.

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Section: B Uncertainty In Optimizationmentioning

confidence: 99%

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

et al. 2023

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“…In this context, recognizing the importance of these strategies is crucial to address the gaps in the current research field and to advance the integration of uncertain wind and PV output scenarios. Goh, Hui Hwang et al [28] developed a renewable energy uncertainty generation scenario analysis methodology that employs a demand response approach to evaluate load demand response and considers carbon capture when modeling microgrids, demonstrating that incorporating as many real-world features as possible can improve the reliability of optimization results. From the above studies, there are fewer IES optimal dispatch studies that consider wind uncertain output scenarios while combining DR and carbon capture, and even when they do, the economics of multiple scenarios are not compared.…”

Section: Introductorymentioning

confidence: 99%

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

Sun,

Zou,

Wen

et al. 2024

Sustainability

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In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

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“…The input discusses various studies and approaches that have been undertaken to optimize and plan stand-alone energy systems that integrate different energy sources and storage solutions [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. These studies utilize different optimization algorithms such as artificial bee colony [13], harmony search [15], elephant herd optimization [18], water wave optimization [21], and particle swarm optimization (PSO) [23], among others.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies focus on specific components of hybrid energy systems, such as photovoltaic (PV) panels, wind turbines, batteries, and electric vehicles, while others consider the overall system design and operation. They also consider uncertain parameters [14,25,26,29] and demand response [21,25] in their optimization models [25,26]. To address uncertainty, different methods are employed, including Monte Carlo Simulation (MCS), analytical approaches, and approximate approaches [32,33].…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty

Alghamdi

2024

Electronics

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In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock’s direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm’s performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model.

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Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty

Cited by 15 publications

References 46 publications

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty

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