Multi-Objective Optimal Scheduling for Multi-Renewable Energy Power System Considering Flexibility Constraints

Abstract

As renewable energy penetration increases, the lack of flexibility in a multi-renewable power system can seriously affect its own economics and reliability. To address this issue, three objectives are considered in this study: power fluctuations on tie-line, operating cost, and curtailment rate of renewable energy. Presented also is an optimal day-ahead scheduling model based on the MREPS for distributed generations with flexibility constraints. The multi-objective particle swarm optimization (MOPSO) algorithm can be applied to obtain a set of Pareto non-dominated solutions for the day-ahead scheduling strategy with the proposed model. By using fuzzy comprehensive evaluation, the optimal compromise solution is determined in the set. The presented method sacrifices a small amount of economy and power fluctuation, but it can reduce the deviation between forecast and realized power fluctuations on the tie-line, while improving the utilization of renewable energy.

Multi-Objective Optimization Scheduling of Active Distribution Network Considering Large-Scale Electric Vehicles Based on NSGAII-NDAX Algorithm

“…The NSGAII-NDAX algorithm significantly reduced economic operational costs compared to the NSGAII algorithm and the algorithms presented in the references [30]- [32]. The economic operational cost was reduced by 9.6% compared to NSGAII, 6.6% compared to reference [30], 6.7% compared to reference [31], and 3.1% compared to reference [32]. These findings indicate that the NSGAII-NDAX algorithm outperforms the other algorithms in terms of minimizing the overall cost of system operation.…”

“…The NSGAII-NDAX algorithm demonstrated superior performance in managing the net load variance of the system. It achieved a reduction of 46.9% compared to NSGAII, 26.4% compared to reference [30], 41.8% compared to reference [31], and 64.8% compared to reference [32]. These results highlight the effectiveness of the NSGAII-NDAX algorithm in mitigating fluctuations in power demand and supply, leading to a more reliable and efficient system operation.…”

“…The NSGAII-NDAX algorithm exhibited improved utilization of solar energy resources compared to the NSGAII algorithm and the algorithms in the references [30]- [32]. It achieved an increase in PV consumption rate of 10.9% compared to NSGAII, 8.5% compared to reference [30], 19.1% compared to reference [31], and 2.3% compared to reference [32]. This indicates that the NSGAII-NDAX algorithm can effectively harness solar energy and promote the integration of renewable energy sources into the system.…”

Special Issue on “Modeling, Analysis and Control Processes of New Energy Power Systems”

“…Yang et al [6] presented an optimal day-ahead scheduling model for a multi-renewable energy power system with distributed generations while satisfying flexibility constraints. Yuan et al [7] proposed a time-of-use pricing strategy for integrated energy suppliers and integrated energy users in the integrated energy systems based on game theory.…”

Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load

“…The interruptible load's response procedure involves responding to the dispatching center's interruption request in a certain amount of time in order to meet the goals of reducing the operational pressure on the system and obtaining a particular financial reward. Economic compensation generally includes capacity compensation and power compensation, as shown in a previous study [31]. The electrical load expression after user n adopts an interruptible load in i period is as follows:…”