Stochastic Bi‐level Trading Model for an Active Distribution Company with DGs and Interruptible Loads

Abstract

With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi-level model-based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals', the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi-level model, where the upper-level problem represents the maximisation of the profit of ADN operator, whereas the lower-level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs. max minimum/maximum energy imported into the ADN (MW) D s 1 , t, q min , D s 1 , t, q max minimum/maximum load of other consumer q in scenario s 1 (MW) G s 1 , t, p min , G s 1 , t, p max minimum/maximum production of generation unit p and scenario s 1 (MW)

Optimal Allocation of Intermittent Distributed Generation under Active Management

“…The economic life of equipment is 20 years, and the discount rate is 0.1. Business load nodes are 1, 3,4,7,11,12,20,22,24,25,32,33. Residential load nodes are 2, 5,6,8,9,13,14,15,16,17,18,19,21,23,26,27,30,31,32,33 The system voltage is 12.66 kV, the total active load is 3.715 MW, the total reactive load is 2.300 MW, and the system parameters are listed in Appendix A, Table A1. The parameters of Weibull distribution are k = 2.30, c = 8.92, the wind power access cost is 6500 yuan/kW, the operation and maintenance cost is 0.3 yuan/kW•h, the environmental protection subsidy is 0.1 yuan/kW•h, the rated illumination intensity of the photovoltaic generator is 1 kW/m 2 , the shape parameter of beta distribution is 0.85, b = 0.85, the photovoltaic access cost is 10,000 yuan/kW, the operation and maintenance cost is 0.2 yuan/kW•h, and the environmental protection subsidy is 0.36/kW•h.…”

“…Generally, the DG single-level programming method is used, and the multiperiod optimal power flow method based on the interior point method is used to solve this kind of model. The other is the DG bilevel programming method, based on bilevel programming theory, which belongs to a very complex mixed integer nonlinear bilevel programming model [18][19][20][21]. Considering the demand response, a two-level solution model is established [18].…”

“…Considering the demand response, a two-level solution model is established [18]. A stochastic programming bilevel model is established through stochastic programming [19]. A bilevel optimization model is presented for the risk assessment of transmission systems [20].…”

Stochastic multi‐objective framework for optimal dynamic planning of interconnected microgrids

“…A reasonable trade off must be respected between the loss of the information and decreasing the computational burden. To implementation backward scenario reduction technique following steps should be performed [24].…”

Controlling a retailer's short‐term financial risk exposure using demand response

“…The perspective of an active distribution company applying DR for resolving grid issues and maximising profits is applied in e.g. [22, 23]. These papers assume an integrated grid operation and retail within the same company, facilitating the co‐optimisation of grid operation and energy deliveries to customers within their network.…”