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The active distribution network is a new solution to the flexible utilization of distributed energy resources to suit the characteristics of the distribution network. Advanced “active” network management is to coordinate “generation, network, load” optimization and achieve the right balance between operational expenditure (OPEX) and capital expenditure (CAPEX). To demonstrate the advancement from introducing an active distribution network, the key features of distribution network planning mainly including traditional models is first introduced. Extensive literature in generic planning is then summarized and categorized in terms of objective functions, system modeling, solution algorithms, and tools. This is followed by an extended review and in-depth discussion of concepts and representative topic developments in optimal active distribution network planning. In contrast to traditional planning, it takes into account the effects from a range of active network interventions that are exercised at differing time scales while capturing the intrinsically stochastic behavior of renewable generation or demand response to produce strategic plans that are robust against a highly uncertain energy future. Finally, a multi-dimensional framework for optimal active distribution network planning is proposed to overcome the limitations of the current state of the art, and the challenges in each stage are also highlighted. [ABSTRACT FROM AUTHOR]

In this article, an optimization model determining renewables penetration limit in power systems is presented. The penetration limit is defined as the enabling renewables output with quantified maximum capacity avoiding the violation of power system operation constraints. Thus, an optimal power flow (OPF)-based model with chance constraints is built and a framework including a Monte Carlo-based genetic algorithm is designed. Moreover, a transient stability verification and correction strategy based on trajectory sensitivity is proposed and modularized in the extended framework. The feasibility of the proposed methodology is verified using several test scenarios, and some related factors are investigated as well. The results indicate that renewables penetration limit can be increased by improving those studied factors. [ABSTRACT FROM PUBLISHER]

A novel method for optimal sizing of energy storage system (ESS) in active distribution networks using state of energy (SOE) function is proposed in this letter. The SOE of ESS in an operational cycle is characterized by an approximately continuous function based on Fourier–Legendre series expansion. Then, the ESS capacity can be determined by the optimized SOE function extreme. The effectiveness and advantages of the proposed method are verified by test cases. [ABSTRACT FROM PUBLISHER]

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills. [ABSTRACT FROM AUTHOR]

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish–Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city. [ABSTRACT FROM AUTHOR]

Areas with hydro power may purchase extra power from the outside power market during dry seasons, which will cause a deviation between the actual and expected power purchase amount due to the inaccurate judgment of the market situation. Because of the uncertainty of price fluctuations, the risk of purchasing power in the real-time market to eliminate this deviation is very high. This paper proposes an innovative trade mode, where the power exchange strategy between multiple areas is adopted through forming an alliance, i.e., one area can use the controllable elements within others, and constructing a monthly and post day-ahead two phase optimization model. The objective function of the monthly stochastic robust optimization considers the power purchase cost to determine the controllable elements dispatch dates for every area in the alliance. Thus, areas can make reasonable dispatch schedules for controllable elements to avoid the resource waste that means more controllable elements are prepared before post day-ahead optimization but less are used after post day-ahead optimization. While the post day-ahead optimization model determines the internal controllable elements dispatch and power exchange amount after the day-ahead market clearing process, users' satisfaction and dispatch schedule changes for energy storage device are also considered. In order to solve the proposed two phase model, the dual principle and linearization methods are used to convert them into mixed-integer linear programming problems that can be effectively solved by the Cplex solver. The study case verifies the power deviation cost decreases with the power exchange strategy and the important role of energy storage devices. [ABSTRACT FROM AUTHOR]