The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: (i) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT), (ii) the effect of subsidies of PV, (iii) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find the optimal solution. Finally, 6 cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. And only when the subsidies of PV reach a certain level will the installation of PV be considered.

As power to gas (P2G) technology gradually matures, the coupling between electricity networks and natural gas networks should ideally evolve synergistically. With the intent of characterizing market behaviors of integrated electric power and natural gas networks (IPGNs) with P2G facilities, this paper establishes a steady-state model of P2G and constructs optimal dispatch models of an electricity network and a natural gas network separately. In addition, a concept of slack energy flow (SEF) is proposed as a tool for coordinated optimal dispatch between the two networks. To study how the market pricing mechanism affects coordinated optimal dispatch in an IPGN, a market equilibrium-solving model for an IPGN is constructed according to game theory, with a solution based on the Nikaido-Isoda function. Case studies are conducted on a joint model that combines the modified IEEE 118-node electricity network and the Belgian 20-node gas network. The results show that if the game between an electric power company and a natural gas company reaches market equilibrium, not only can both companies maximize their profits, but also the coordinated operation of the coupling units, i.e., gas turbines and P2G facilities, will contribute more to renewable energy utilization and carbon emission reduction.

This paper aims to optimize total energy costs in an operational model of a novel energy hub (EH) in a residential area. The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic (PV) generation, solar heat exchanger (SHE), and a battery energy storage system (BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price, capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.

Large-scale wind farms (LSWFs) have become a popular form of exploiting abundant wind energy. However, the integration of LSWFs presents challenges to the volt/var control (VVC) of power grids. In this regard, various VVC methods for power grids with connections of LSWFs have been proposed from different viewpoints. This paper provides a comprehensive review on VVC for power grids with connections of LSWFs. Firstly, the challenges presented by LSWFs to VVC are investigated with regard to the uncertainty of power flow and voltage, conflicts of VVCs among interconnected power grids, operation of discrete devices, voltage stability, and reactive power market. Secondly, an overview on current research regarding VVC methods for LSWFs connected power grids is presented, where the VVC methods are classified into three categories: decentralized VVC, centralized VVC and hierarchical VVC. The three categories of VVC methods are analyzed and compared in terms of their advantages, disadvantages and applications. In addition, drawbacks of current research are concluded. Finally, to overcome the drawbacks presented in current research, this paper provides directions for future research, including event-triggered VVC methods, big data techniques, reactive power markets, energy storage systems, and VSC based HVDC.

In this paper, we propose an energy storage capacity optimization (ESCO) method for grid-connected microgrid systems (MSs) considering multiple time scale uncertainty coupling. First, an envelope model and a box uncertainty model are, respectively, employed to characterize the stochastic uncertainties and the forecasting error uncertainty of the renewable energy (RE) power supply and load demand. Based on the above two uncertainty characterization models, the energy balance ability indicator and the robustness coordination cost indicator are proposed, which are used to quantitatively analyze the MS energy balance state probability for different ESCO schemes and the economic cost of improving the robustness of the MS optimal operation, respectively. Then, an ESCO model, where the stochastic uncertainty and forecasting error uncertainty of the RE power supply and load demand are, respectively, considered in the long-time-scale investment decision of the energy storage capacity and the short-time-scale operation optimization of the energy storage system, is established, with the goal of realizing an MS that operates in a grid-friendly and economical manner. Furthermore, a new multi-objective compound differential evolution (MO-CDE) algorithm is designed to solve the energy storage capacity collaborative optimization model efficiently. Finally, simulations are conducted to verify the rationality and effectiveness of the proposed model and method.

With the growing penetration of grid-connected microgrids (MGs), utility grid (UG) faces the challenge of high-level fault current during fault ride-through (FRT) of MGs. To tackle this challenge, a fault current hierarchical limitation (FCHL) strategy is proposed, which consists of four levels: 1) fault current primary limitation control (FCPLC) is based on the proportional-resonant (PR) current controller; 2) fault current secondary limitation control (FCSLC) is formulated for current generation of grid-connected inverters (GCIs) with peak fault current limitation; 3) fault current tertiary limitation control (FCTLC) is formulated for the phase angle shifting of MG’s fault current to neutralize its effect on UG’s fault current; 4) fault current quartus limitation control (FCQLC) is developed for the coordinated operation of parallel GCIs within grid code requirement. Extensive tests have validated the effectiveness of proposed strategy with a successful FRT while the MG’s output fault current has a minute effect on the UG’s fault current amplitude.