Abstract
Prosumers are agents that both consume and produce energy. This article studies the optimal energy management of a residential prosumer which consists of a renewable power plant and an energy storage unit. Energy could stream among power grid, renewable plant, storage unit, and demand, providing a highly flexible energy supply and the opportunity of arbitrage. To capture the uncertainty of renewable generation and electricity price, as well as the rolling horizon feature of the multiperiod energy management, the problem is formulated as a robust data-driven dynamic programming (RDDP). Kernel regression is utilized to build the empirical conditional distribution in a data-driven manner, and all candidates that reside in a Wasserstein metric-based ambiguity set are taken into account to tackle the inexactness of the empirical distribution. The RDDP can be transformed into a series of convex optimization problems with cost-to-go functions in their constraints. The piecewise linear expression of the cost-to-go function is retrieved from dual linear programs. Through such an analytical expression of cost-to-go functions, the RDDP can be solved via backward induction, unlike the popular stochastic dual dynamic programming technique that incorporates forward and backward passes. Case studies validate the performance and advantage of the proposed RDDP approach.

Abstract
In spite of the significant advance achieved in the development of optimal power flow (OPF) programs, most of the solution methods reported in the literature have considerable difficulties in dealing with different-nature objective functions simultaneously. By leveraging recent progress on the semidefinite programming (SDP) relaxations of OPF, in the present article, attention is focused on modeling a new SDP-based multiobjective OPF (MO-OPF) problem. The proposed OPF model incorporates the classical epsilon-constraint approach through a parameterization strategy to handle the multiple objective functions and produce Pareto front. This article emphasizes the extension of the SDP-based model for MO-OPF problems to generate globally nondominated Pareto optimal solutions with uniform distribution. Numerical results on IEEE 30-, 57-, 118-bus, and Indian utility 62-bus test systems with all security and operating constraints show that the proposed convex model can produce the nondominated solutions with no duality gap in polynomial time, generate efficient Pareto set, and outperform the well-known heuristic methods generally used for the solution of MO-OPF. For instance, in comparison with the obtained results of NSGA-II for the 57-bus test system, the best compromise solution obtained by SDP has 1.55% and 7.42% less fuel cost and transmission losses, respectively.

Abstract
This article presents an analytical approach based on Extended Kalman Filter (EKF) for nodal pricing in distribution networks containing private distributed generation (DG). An appropriate nodal pricing policy can direct active distribution network (ADN) to optimal operation mode with minimum loss. However, there are several crucial challenges in nodal pricing model such as: equitable loss allocation between DGs, obtain minimum merchandising surplus (MS), and equitable distribution of remuneration between DGs, which is difficult to achieve these goals simultaneously. However, in the proposed method, the issue was embedded in the form of the EKF updates. The measurement update reduces the MS, and in the time update, DG's nodal prices as state variables are modified based on their contribution to the loss reduction. Therefore, all aspects of the problem are considered and modeled simultaneously, which will prepare a realistic state estimation tool for distribution companies in the next step of operation. The proposed method also has the ability to determine the nodal prices for distribution network buses in a wide range of power supply point prices (PSP), which other methods have been failed, especially at very low or high PSP prices. Eventually, using the new method will move system towards to the minimum possible losses with the equitable condition. The application of the proposed nodal pricing method is illustrated on 17-bus radial distribution test systems, and the results are compared with other methods.

Abstract
Growing demand for energy carriers has led to an increased interest in developing and managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable resources as well as the uncertain electrical and thermal demands imposes significant challenges for the operation of microgrids. Motivated by this, the paper leverages a min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads. The proposed model is linearized and solved using the column-and-constraint generation (C&CG) procedure that decomposes the framework into a master problem and a subproblem. The master problem minimizes the unit commitment cost, while the sub-problem determines the dispatch cost associated with the worst realization of uncertainties via a max min objective function. Also, polyhedral uncertainty sets are defined with budget of uncertainty parameter that adjusts the trade-off between the operation cost and the degree of robustness. The effectiveness of the framework is assessed and discussed via a 21-node energy hub-based microgrid. It can be seen that the solution immunizes against all realizations of uncertainties, whereby increasing the budget of uncertainty and the forecast error, the system robustness is improved. Moreover, the dual variables of the subproblem are converted to the primary variables in order to evaluate the unit commitment and energy dispatch results.

Abstract
This article proposes a unified solution to address the energy issues in net-zero energy building (ZEB), as a new contribution to earlier studies. The multicarrier energy system, including hydro-wind-solar-hydrogen-methane-carbon dioxide-thermal energies is integrated and modeled in ZEB. The electrical sector is supplied by hydro-wind-solar, combined heat and power (CHP), and pumped hydro storage (PHS). The thermal sector is supplied by CHP, thermal boiler, and electric heating. The hydrogen storage system and Methanation process operate as the interface energy carriers between the electrical and thermal sectors. The carbon dioxide (CO2) of the ZEB is captured and fed into the Methanation process. The purpose is minimizing the released CO2 to the atmosphere while all the electrical-thermal load demands are successfully supplied considering events and disruptions. The model improves simultaneously the energy resilience and minimizes the environmental pollutions. The results demonstrate that the developed model reduces the CO2 pollution by about 33 451 kg per year. The model is a resilient energy system that can handle all failures of components. The model can efficiently handle 26% increment in the electrical loads and 110% increment in the thermal loads.

Abstract
The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids (mu G), to substitute mu Gs arrangements for effectively coping with perturbations. This flexible structure not only could potentially possess the strength to recover quickly, but also ensures the supply of vital loads and preserves functionalities under any contingency. To achieve these targets, this article examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing mu Gs. In this endeavor, after designing the mu Gs by determining a mix of heterogeneous generation resources and allocating remotely controlled switches, the mu Gs operational scheduling is decomposed into interconnected and islanded modes. The main intention in the grid-tied state is to maximize the mu Gs profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the mu Gs less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.