Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading.

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A unit commitment formulation satisfying AC optimal power flow constraints models the resource optimization problem accurately, but is challenging to solve for traditional transmission systems. Integration of generation based on renewable energy sources is often limited by transmission congestion issues in existing grids. For such grids, AC to HVDC conversion schemes are viable and attractive options for capacity expansion. In this respect, we utilize the structural properties of a hybrid AC/HVDC grid architecture with a specific topology to enable an exact mixed integer conic relaxation of the aforementioned problem. The simulation results for the PJM 5-bus system show that the relaxation is exact for the hybrid architecture. Further, it alleviates the network congestion leading to a substantial reduction in generation cost at normal and increased load conditions compared to the reference AC grid. Moreover, the hybrid architecture improves the utilization of the grid to accommodate more demand and generation.

Energy management system of a microgrid (MG) is responsible for determining its optimal generation schedule which fulfills the load demand while satisfying several constraints related to the equipment and system operation. When many MGs are interconnected, it is sometimes preferable to exchange power among MGs in such a manner so as to minimize their operating cost while reducing dependency on the main grid. This paper proposes a cost based power sharing scheme combined with a more realistic EMS scheme. These two schemes are implemented in two control layers which communicate with each other to optimize MG operation. Simulation results demonstrate the benefits of the proposed approach.

This study proposes a new trust-region based sequential linear programming algorithm to solve the AC optimal power flow (OPF) problem. The OPF problem is solved by linearizing the cost function, power balance and engineering constraints of the system, followed by a trust-region to control the validity of the linear model. To alleviate the problems associated with the infeasibilities of a linear approximation, a feasibility restoration phase is introduced. This phase uses the original nonlinear constraints to quickly locate a feasible point when the linear approximation is infeasible. The algorithm follows convergence criteria to satisfy the first order optimality conditions for the original OPF problem. Studies on standard IEEE systems and large-scale Polish systems show an acceptable quality of convergence to a set of best-known solutions and a substantial improvement in computational time, with linear scaling proportional to the network size.The OPF problem optimizes the total operating cost to support efficient 2 operation of power systems while satisfying system constraints for a nominal 3 state [1]. In practice one needs to solve a security-constrained OPF (SC-OPF) 4 problem which takes into account the possibility of a sudden failure of a single 5 component (generator, transmission line, transformer, etc) in the system. This 6 is known as the N−1 security criterion [1, 2]. The OPF problem without se-7 curity constraints has been extensively investigated in the literature (see, for 8 instance [2], and references therein). This paper addresses the OPF problem 9 for simplicity, but the benefits of our approach extend to the context of the 10 SC-OPF problem as well. It is well-known that the OPF problem is nonlinear 11 and nonconvex in nature, potentially having multiple equilibrium points. Hence 12 searching for a global solution is in principle 3, 4, 5]). Electricity 13 market clearing strategies are mainly based on nodal prices, which are the dual 14 variables of power balance constraints of the OPF problem. This highlights 15 the importance of the convexity and scalability features for any algorithm to 16 use in OPF calculations [1, 6]. In addition to this, real-world OPF problems 17 involve very large numbers of decision variables. This makes them challenging 18 for a solution technique, both in terms of memory and computational time re-19 quirements. Consequently there is a great need for computationally efficient 20 techniques which can handle the nonconvex AC network constraints. 21 In the context of OPF, solution approaches, such as linear programming 22 (LP) [6, 7, 8], quadratic programming (QP) [9], Lagrangian relaxation [10], 23 and interior-point (IP) methods [11] have been extensively investigated in the 24 literature. It is worth noting that, among all these approaches, IP methods 25 have emerged as a promising direct solution approach for OPF problems. IP 26 methods have proven to be a viable computational alternative for the solution of 27 large-scale OPF problems [12]. The primal-dual log...

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This work proposes a cooperative trading scheme for the robust optimal power dispatch and reserve allocation in a multiple-microgrid (MMG) system comprising four microgrids (MGs). This scheme interfaces with a robust optimization (RO) model which accounts for the uncertainties in the renewable energy source generation, the load demand and the energy market prices using a scenario based worst-case exploration technique. A coordinating entity in the MMG system called a power sharing operator manages the day-ahead energy and reserve trading between the MGs and also with the main grid, thereby enabling market clearing within the MMG system. The proposed scheme is based on the iterative sub-gradient method. The results showcase the convergence characteristics of the proposed scheme. Moreover, the trade-off between the energy and reserve costs in each MG are also analysed. Finally, the economic benefits gained by the MMG system under the proposed RO based cooperative trading scheme are evaluated.