Flexible loads, most of which are on/off appliances, are playing an important role in keeping the balance between power generation and demand. In this paper, a distributed consensus-based control strategy is proposed for the economic load dispatch in the frequency regulation. In order to avoid the complexity caused by the discrete cost characteristics, the continuous cost function of each user is fitted by the least squares method in advance. To minimize the overall load dispatching costs during the frequency regulation, Incremental Cost Consensus (ICC) algorithm is proposed in the load control. The economic load dispatch is realized through the convergence of the Incremental Cost (IC) of users. Case studies are provided to verify the effectiveness of the proposed method, with comparisons of different discretization methods.

Incremental Cost Consensus Algorithm for On/Off Loads to Enhance the Frequency Response of the Power System

Abstract Renewable energy generation represents a unique solution to ensure the sustainable development of society. However, its fluctuating nature can disturb the energy balance mechanism of the power grid. In microgrids powered by renewables, the issue is even more critical. Fossil fuel generation typically supplements renewables but storage and demand response can be more flexible and cost effective. This paper is an overview of recent undertakings that present storage and demand response techniques as solutions for the stable operation of renewable energy microgrids. The critical analysis of the recent papers in this area reveals that the parameters used for modeling storage have been simplified (efficiency, dynamic behavior at fast rate of discharge, aging…) and that the demand response incentives have been assumed to be enough for users to be willing to participate in demand response programs. These assumptions make the proposed solutions too inaccurate to be implemented on the field yet. If renewables have to be implemented on a large scale, specific and accurate models have to be used. By building on the current research presented here, much work can be converted into real advances in the field of renewable energy integration in microgrids.

A critical review on the utilization of storage and demand response on the implementation of renewable energy microgrids

This research report investigates a novel optimization approach for the economic dispatch problem (EDP) based on the weighted sum of generators’ costs under supply-demand balance. Unlike conventional approaches, we present a distributed optimization approach that ensures optimality using weighted incremental cost (IC) consensus and sign-consensus error convergence. We can apply the optimization of a weighted sum of generators’ costs to address several constraints, such as capacity and environmental constraints, in addition to the supply-demand balance. The proposed distributed weighted incremental cost consensus approach has been applied to the IEEE-30 bus and IEEE-118 bus systems over a communication topology. The results indicate the efficacy of weights to address generation constraints and the convergence of weighted ICs under supply-demand balance.

/pdf/optimality-condition-and-distributed-optimization-for-12pio7w0.pdf

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

A novel consensus-oriented distributed optimization scheme with convergence analysis for economic dispatch over directed communication graphs

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

/pdf/hybrid-metaheuristic-model-for-optimal-economic-load-2blf6iex.pdf

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

In the past few years, Battery Energy Storage System (BESS) has been found of great potential in supporting the frequency control. Increasing attentions have been given to the control strategy of BESS. In this paper, a distributed control method considering the life-loss cost is proposed for BESS. Based on the multi-agent system, the Incremental Cost Consensus (ICC) algorithm is applied to minimize the life-loss cost of BESS. In order to improve the control performance, parameters of the system are optimized by Genetic Algorithm (GA). The simulation results are provided to verify the effectiveness of the proposed approach.

/pdf/battery-energy-storage-system-based-on-incremental-cost-1it1r7dlds.pdf

Battery Energy Storage System Based on Incremental Cost Consensus Algorithm for the Frequency Control

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Battery Energy Storage System Based on Incremental Cost Consensus Algorithm for the Frequency Control

Incremental Cost Consensus Algorithm for On/Off Loads to Enhance the Frequency Response of the Power System