Stochastic Flexible Power System Expansion Planning, Based on the Demand Response Considering Consumption and Generation Uncertainties

• Published in: Sustainability Vol. 15; no. 2; p. 1099
• Main Authors: Toolabi Moghadam, Ali, Bahramian, Bahram, Shahbaazy, Farid, Paeizi, Ali, Senjyu, Tomonobu
• Format: Journal Article
• Language: English; Japanese
• Published: Basel MDPI AG 06.01.2023
• Subjects: Capital costs; Construction costs; Consumption; consumption and generation energy uncertainties; demand response; dynamic planning of the power system; flexibility; hybrid evolutionary algorithm; Decision theory; Demand side management; Flexibility; Genetic algorithms; Linear programming; Mathematical models; Monte Carlo simulation; Operating costs; Optimization; Planning; Supply & demand; Sustainability
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15021099

This paper presents the generation and transmission expansion planning (GTEP) considering the switched capacitive banks (SCBs) allocation in the power system, including the demand response program (DRP). This scheme is based on the system flexibility. The objective function of the scheme minimizes the expected planning cost that is equaled to the summation of the total construction costs of the SCBs, the generation units (GUs) and the transmission lines (TLs), and the operating cost of the GUs. It is concerned with the AC power flow constraints, the planning-operation model of the mentioned elements, the DRP operation formulation, and the operating and flexibility limits of the network. In the following, the scenario-based stochastic programming is used to model the uncertainty parameters, such as the load and renewable power of wind farms. Then, the hybrid evolutionary algorithm, based on the combination of the crow search algorithm and the grey wolf optimizer, is used to determine the optimal point with the approximate unique solution. Finally, the scheme is applied on the transmission networks, the numerical results confirm the capabilities of the proposed scheme in simultaneously improving the flexibility, operation, and economic situation of the transmission network, so that the hybrid algorithm achieves the optimal solution in a shorter computation time, compared with the non-hybrid algorithms. This algorithm has a low standard deviation of about 92% in the final response. The proposed scheme with the optimal planning of the lines, sources, and capacitor banks, together with the optimal operation of the DRP succeeded in improving the energy loss and the voltage deviation by about 30–36% and 25–30%, compared with those of the power flow studies.