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This paper presents the planning of solar photovoltaics (PV), battery energy storage system (BESS) and gas-fired micro turbine (MT) in a coupled micro gas and electricity grid. The proposed model is formulated as a two-stage stochastic optimization problem, including the optimal investment in the first stage and the optimal operation in the second stage. To better understand the mutual interactions between electric and heat energy, the gas network models are taken into account. As a result, the fuel availability and price of the gas-fired MT can be explicitly modeled and analyzed. Moreover, to enhance the computational efficiency of the formulated mixed-integer quadratic programming problem, the point estimation method is used as the scenario reduction technique. The effectiveness of the proposed model is verified on a 14-bus coupled micro energy grid. Based on the case studies, the proposed two-stage planning model can identify a planning solution with the objective value of $99.3104, which is comprised of the daily capital recovery cost of $20.5070, the daily operating cost of $78.8034 for the coupled micro gas and electricity grid. Comparative studies demonstrate that the proposed approach can help the microgrid operator identify feasible and optimal planning solutions, and provide valuable guidance for energy infrastructure expansion from an integrated perspective. [ABSTRACT FROM AUTHOR]

• A new renewable energy trading system is proposed for residential communities. • The proposed energy trading system is highly scalable. • Homomorphic Encryption technology is introduced to protect user privacy. • Extensive numerical simulations are conducted to validate the proposed system. This paper proposes a new trading framework for enabling energy trading between end energy customers and a small-capacity Renewable Energy Plant (REP) in urban environments. To relieve the communication burden incurred by a large number of end customers who submit bids to purchase energy from the REP, the energy trading process is designed on a community basis. The end customers are grouped into multiple communities. With this approach, the proposed energy trading system coordinates the REP, the community energy management system in each community, and the local energy management system of each customer. The homomorphic encryption technology is integrated into the energy trading framework to enable the energy trading to be performed and settled without exposing individual customer's bidding energy purchase price and amount values. Extensive numerical simulations are conducted to validate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

• Study the vision of engaging networked data centers into distributed energy markets. • Propose a decision-making model to optimize the bidding decisions and energy resource operations of networked data centers in multiple geo-distributed local energy markets. • Conduct comprehensive numerical simulations to validate the benefit the cloud service provider can obtain from local energy markets. Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs' cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia's electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high. • An energy sharing platform is presented for renewable distributed generation. • An independent energy management strategy can be customized for each participant. • The call auction model of stock exchange is cited in the energy sharing platform. • The maximum transaction volume is applied in the call auction model. • The award of energy sharing can be self-ruled by participants. [ABSTRACT FROM AUTHOR]

• A sharing platform framework with energy and capacity sharing of hybrid energy storage system (HESS) is proposed. • Detailed technic-economic analysis has been conducted. • The characteristics of electrical and thermal demands are captured, in order to assist the energy management of HESS. • Flexibility from multi-energy systems studied is including electricity, heat and gas sector and energy storage. • Energy sharing and flexibility of end-users are also considered. There is already a large amount of energy storage system (ESS) and demand response potential in the power, heat and gas system, which can be used to promote a cost-effective transition to low-carbon and renewable energy. This paper proposes an energy sharing platform to effectively integrate power, thermal and gas systems of different sizes to balance the fluctuation of renewable energy and improve the system reliability. In a potential application in the hospitality industry, hotels can jointly share and rent ESS. This sharing platform uses a hybrid energy storage system (HESS), comprising BESS and thermal energy storage system (TESS). Unlike BESS, TESS is cost-effective and can be provided by hot water tanks as short-term energy storage. The capacity and energy sharing method of the hybrid BESS and TESS system is provided, including the detailed rental model of HESS in the operation economy. Moreover, the coupled energy supply chain is also considered, the combined utilization of thermal, gas and electricity sharing is achieved. The proposed model is evaluated on the IEEE 18-bus system with the real historical data in Australian energy market, and the cost-benefit analysis is also provided. [ABSTRACT FROM AUTHOR]