Abstract
This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.
Highlights
With the shortage of fossil energy and the deterioration of the natural environment, renewable energy sources (RESs) represented by wind power and photovoltaic (PV) have been rapidly developed.With higher distributed energy resources integration, the traditional distribution network is gradually becoming an active distribution network (ADN) and the optimal operation of the distribution network is facing new challenges [1,2,3]
The reactive power optimization (RPO) for ADN is often formed as a mixed integer nonlinear programming (MINLP) problem, which should deal with both continuous control variables such as distributed generations (DGs) output and discrete control variables, such as capacitor banks (CBs) and on-load tap changer (OLTC) [6,7]
InComparison order to reflect the advantages of the proposed mixed integer SOCP (MISOCP) model in this paper, the intelligent method
Summary
With the shortage of fossil energy and the deterioration of the natural environment, renewable energy sources (RESs) represented by wind power and photovoltaic (PV) have been rapidly developed.With higher distributed energy resources integration, the traditional distribution network is gradually becoming an active distribution network (ADN) and the optimal operation of the distribution network is facing new challenges [1,2,3]. Active power optimization is called economic dispatch, which often aims to minimize the total day-ahead or real-time operation cost by deciding the output of distributed generations (DGs) and charge–discharge power in the distribution system. RPO is an important measure to ensure safe and economic operation of distribution network, which can regulate the system voltage profile and power flow, as well as reduce the active power losses [4,5]. The RPO for ADN is often formed as a mixed integer nonlinear programming (MINLP) problem, which should deal with both continuous control variables such as DG output and discrete control variables, such as capacitor banks (CBs) and on-load tap changer (OLTC) [6,7]. The RPO problem is essentially nonconvex due to the nonconvex and nonlinear power flow equations, which
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