An improved weighted mean of vectors algorithm for microgrid energy management considering demand response

Abstract

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques.

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

“…This approach involves using the best location vector , the second-best location vector , and the third-best location vector based on the objective function value of the new location vector relative to the population size. The new mutation position vector is then calculated as: Then, the next location is updated using the following equation 48 : Finally, the optimal solution can be updated as follows 49 : The diagram in Fig. 1 illustrates the flowchart of the Leader supply-demand-based optimization (LSDO) algorithm.…”

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

“…This approach, which considers the objective function values among the population, is a highly effective strategy for mitigating these issues and bolstering the original WaOA algorithm's performance and robustness. The new mutation location vector $${X}_{i}({mut})$$is then determined as outlined in Alamir et al 29 $$${X}_{i}({mut})={X}_{i}({new})+2\times \left(1-\frac{t}{\mathrm{Max\_}{it}}\right)\times (2\times {rand}-1)\left(2\times {X}_{\mathrm{best}}^{t}-\left({X}_{\mathrm{best}-1}^{t}+{X}_{\mathrm{best}-2}^{t}\right)\right)+(2\times {rand}-1)\left({X}_{\mathrm{best}}^{t}-{X}_{i}({new})\right).$$$…”

Environmental–Economic Analysis of Multi-Node Community Microgrid Operation in Normal and Abnormal Conditions—A Case Study of Indonesia

“…The authors suggested the improved weighted mean of the vectors algorithm in [22] to solve the day-ahead operation management of MGs to minimize the operation cost of the system. However, the operation of the system in abnormal conditions is not studied in this paper.…”