A Day-Ahead Chance Constrained Volt/Var Control Scheme With Renewable Energy Sources by Novel Scenario Generation Method in Active Distribution Networks

Abstract

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods. , sh j u Continuous state variable on bus j at hour h of scenario s . Abbreviation RESs Renewable energy sources. VVC Volt/var control. MCS Monte Carlo sampling. OLTC On-load tap changer. MINLP Mixed integer nonlinear programming. CCP Chance constrained programming. SSC Switchable shunt capacitors. PV Photovoltaic. WTG Wind turbine generator.

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

“…However, stochastic variables, such as SPG, rarely follow a specific distribution, which leads to the generation of simplified scenarios that do not share the same statistical properties as the real observations. The most commonly used sampling technique is Monte Carlo sampling (MCS) [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. In [33], MCS was used to sample the assumed error distribution of PV power curtailment forecasts generated by gated recurrent units (GRUs).…”

“…In [27], lattice MCS was used combined with roulette wheel selection (RWS) to efficiently generate scenarios. In [35], a seven-step distribution model was used to discretize the data distribution in order to generate less-but higher quality-scenarios. In both cases, the generated results were similar to those of traditional MCS, while maintaining a much smaller computational cost.…”

“…A higher standard deviation increases the ability to capture extreme scenarios, often at the cost of scenario representativeness. On the other hand, several distributions are used to fit the historical observations of solar irradiance, such as beta [3,30,36], Weibull [41], normal [35,50,52], and lognormal [39,40]. Beta distribution has been the traditional choice in fitting solar irradiance data; however, in [41], better results were achieved with the Weibull distribution.…”