Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization

Abstract

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment

“…The loss of load probability is used as the reliability evaluation index, and risk decision is made according to the risk attitude of system dispatching decision maker. Based on the method of scenario analysis, a two-stage stochastic optimization model including unit start-up and shutdown decision making and generation planning was established in reference [37]. The cost of wind curtailment and loss of load were included in the objective function.…”

Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve

“…Table 2 reports a non-exhaustive summary of the possible optimization fields with the related goals, variables, and constraints. There are numerous recent studies which highlight the importance of the application of multi-objective optimization algorithms in the energy engineering field [80][81][82][83][84][85]. A multiobjective optimization is a reliable approach as in these types of problems, the intention is to obtain a collection of solutions which satisfy the objective functions to various degrees, with which a Pareto frontier can be formed, presenting the probable answers through the function zone [83].…”

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

“…For the overall solution of the optimal dispatching model of FIES, this paper first adopts the scenario-reduction method to describe the typical output scenarios of wind power and photovoltaic units. According to the historical output data of wind power and photovoltaic generators, the FCM-CCQ method [40] is used for cluster analysis and the historical output scenarios are clustered into a rational number of typical scenarios. Finally, aiming at the minimum expected value of total operating cost, the optimal scheduling optimization strategy of IES is solved in different typical scenarios.…”