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Abstract:A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user's demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.

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Demand response (DR) can offer a wide range of advantages for electrical systems by facilitating the interaction and balance between supply and demand. However, DR always requires a central agent, giving rise to issues of security and trust. Besides this, differences in user response cost characteristics are not taken into consideration during incentive pricing, which would affect the equal participation of users in DR and increase the costs borne by the electricity retail company. In this paper, a blockchain-enabled DR scheme with an individualized incentive pricing mode is proposed. First, a blockchain-enabled DR framework is proposed to promote the secure implementation of DR. Next, a dual-incentive mechanism is designed to successfully implement the blockchain to DR, which consists of a profit-based and a contribution-based model. An individualized incentive pricing mode is adopted in the profit-based model to decrease the imbalance in response frequency of users and reduce the costs borne by the electricity retail company. Then, the Stackelberg game model is constructed and Differential Evolution (DE) is used to produce equilibrium optimal individualized incentive prices. Finally, case studies are conducted. The results demonstrate that the proposed scheme can reduce the cost borne by the electricity retail company and decrease the imbalance among users in response frequency.

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The integrated energy system is an effective way to achieve carbon neutrality. To further exploit the carbon reduction potentials of IESs, an optimal dispatch strategy that considers integrated demand response and stepped carbon trading is proposed. First, an integrated demand response (IDR) pricing approach is proposed based on the characteristics of different load types. Classify multi-energy loads into curtailable and substitutable loads, and incentivize both loads through a price elasticity matrix and low-price energy in the same period. Then, to better incentivize IESs to reduce carbon emissions, a stepped pricing mechanism was introduced in the carbon price. Finally, an optimal dispatch model is developed with an objective function that minimizes the sum of energy purchase cost, carbon trading cost, and operation and maintenance (O&M) cost. Considering the high-dimensional and non-linear characteristics of the model, an improved differential evolution (DE) algorithm is introduced in this paper. In addition, this paper also analyzes the effects of the stepped carbon trading parameters on the optimal dispatching results of the system in terms of carbon trading base price, carbon emission interval length, and carbon price growth rate. Compared to the case of adopting a single IDR model or a single stepped carbon trading, carbon emissions from the IESs decreased by 6.28% and 3.24%, respectively, while total operating costs decreased by 1.24% and 0.92%, The results show that the model proposed in this paper has good environmental and economic benefits, and the reasonable setting of stepped carbon trading parameters can effectively promote the low-carbon development of IESs.

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