Search

The low-carbon economic operation of integrated energy systems (IES) cannot be separated from the carbon trading and green certificate trading market. Therefore, a low-carbon market mechanism and optimization method for IES is proposed. First, we establish a "green-carbon" offset mechanism to realize the conversion from tradable green certificate (TGC) to carbon quotas to offset system's carbon emissions. Second, considering the impact of market incentives and users' consumption behavior on IES, an energy management method of IES is put forward based on Stackelberg game. IES operators as leaders decide energy prices and trading strategies of carbon, TGC and multi-energy. Energy users as followers participate in the integrated demand response based on energy price. The game is solved by an improved adaptive catastrophic genetic algorithm and CPLEX solver. Finally, we take an industrial park in China as an example to analyze. The results show that the proposed method can significantly reduce the system's carbon emissions while improving the benefits for both IES and consumers. • A mechanism is proposed to offset system carbon emissions with green certificates. • An intelligent pricing and optimal method is proposed based on Stackelberg game. • Carbon capture and power-to-gas technology are used to reduce carbon emissions. • Key parameters sensitivity of the offset mechanism is analyzed for specific cases. [ABSTRACT FROM AUTHOR]

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods. [Display omitted] • Three-stage steel plant MEMG low-carbon operation coordinated with process optimization. • Generalized continuous/discontinuous process modelling for steel production. • Uncertain intervals of renewable generation tackled with SO and SAA in different stage. [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]

Several electricity tariffs have emerged for Demand Side Management (DSM) and residential customers are faced with challenges to choose the plan satisfying their personal needs. Electricity Plan Recommender System (EPRS) can alleviate the problem. This paper proposes a novel EPRS model named EPRS with Electrical Instruction-based Recovery (EPRS-EI), which is a dual-stage model consisting of feature formulation stage and recommender stage. In the feature formulation stage, matrix recovery with electrical instructions is applied to recover appliance usages, and the recovered data is set as features representing customers' living patterns. In the recommender stage, Collaborative Filtering Recommender System (CFRS) based on K-Nearest Neighbors (KNN) and adjusted similarity is applied to recommend personal electricity plans to customers based on the above features. Different from other EPRS models, EPRS-EI is the first model utilizing matrix recovery methods and similarity computation with electrical instructions. With these electrical instructions, the proposed model is able to utilize more explicit features and recommend more personalized plans. We then apply EPRS-EI to predict the testing customers' preference for electricity plans. Simulation results on recovering electricity data and their applications in EPRS confirm the effectiveness of the proposed methods in comparison to state-of-the-art methods, with 93.56%–94.85% customers correctly recommended. • Proposing an electricity plan recommender system (EPRS) with matrix recovery. • Appliance classification avoids trivial changes in recovering appliance usages. • Total electricity usages are set as a recovery instruction in recovering data. • Novel matrix recovery could recover up to 38.15% more data. • 93.56%–94.85% of users can obtain effective electricity plans with novel EPRS. [ABSTRACT FROM AUTHOR]