The technology advancement and cost decline of renewable and sustainable energy increase the penetration of distributed energy resources (DERs) in distribution systems. Transactive energy helps balance the local generation and demand. Peer-to-peer (P2P) energy trading is a promising business model for transactive energy. Such a market scheme can increase the revenue of DER owners and reduce the waste of renewable energy. This article proposes an equilibrium model of a P2P transactive energy market. Every participant seeks the maximum personal interest, with the options of importing or providing energy from/to any other peer across different buses of the distribution network. The market equilibrium condition is obtained by combining the Karush–Kuhn–Tucker conditions of all problems of individual participants together. The energy transaction price is endogenously determined from the market equilibrium condition, which is cast as a mixed-integer linear program and solved by a commercial solver. The transactive energy flow is further embedded in the optimal power flow problem to ensure operating constraints of the distribution network. We propose a remedy to recover a near optimal solution when the second-order cone relaxation is inexact. Finally, a case study demonstrates that the proposed P2P market benefits all participants.
This paper evaluates the economic impact of short-term and long-term energy storage capacity on power system operation cost. First, the unit commitment (UC) model with short-term and long-term energy storage comprising a year-round hourly operation simulation is established to minimize the optimal operation cost. By introducing the convex hull of the original feasible region of decision variables, the integer variables in UC model are relaxed as continuous variables, transforming the UC problem as a linear programming (LP). Then, by regarding the power and energy capacities as parameters, the LP is cast as a multiparametric linear programming (mp-LP), where the optimal value function (OVF) is explicitly expressed as a piecewise linear function of the capacity parameters. An approximation method is used to calculate the analytical optimal value function and critical regions. The analytical expression of OVF contains rich sensitivity information. Finally, case studies validate our proposed method and provide visualization results. The sensitivity information of analytical OVF is exploited. As a potential application, the economic evaluation results could be applied in energy storage capacity sizing.
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