With the increasing proportion of distributed power supplies connected to the power grid, the application of a battery energy storage system (BESS) to a power system leads to new ideas of effectively solving the problem of distributed power grid connections. There is obvious uncertainty involved in distributed power output, and these uncertainties must be considered when optimizing the scheduling of virtual power plants. In this context, scene simulation technology was used to manage the uncertainty of wind power and photovoltaic output, forming a classic scenario. In this study, to reduce the influence of the uncertainty of wind and photovoltaic power output on the stable operation of the system, the time-of-use (TOU) prices and BESS were incorporated into the optimal scheduling problem that is inherent in wind and photovoltaic power. First, this study used the golden section method to simulate the wind and photovoltaic power output; second, the day-ahead wind and photovoltaic power output were used as the random variables; third, a wind and photovoltaic power BESS robust scheduling model that considers the TOU price was constructed. Finally, this paper presents the Institute of Electrical and Electronics Engineers (IEEE) 30 bus system in an example simulation, where the solution set is based on the Pareto principle, and the global optimal solution can be obtained by the robust optimization model. The results show that the cooperation between the TOU price and BESS can counteract wind and photovoltaic power uncertainties, improve system efficiency, and reduce the coal consumption of the system. The example analysis proves that the proposed model is practical and effective. By accounting for the influence of uncertainty of the optimal scheduling model, the actual operating cost can be reduced, and the robustness of the optimization strategy can be improved.

The energy consumed by buildings makes up a significant part of total social energy consumption. The energy use rate of the traditional cooling and heating unit is low. A distributed cooling, heating, and power (CHP) system can achieve cascade use of energy and reduce the long-distance transportation of energy. Along with the wide use of ground-source heat pumps and energy storage technology, the combined cooling, heating, and power (CCHP) system coupled with a ground-source heat pump and energy storage technology is increasingly being used. Firstly, we proposed the construction of a CCHP system driven by distributed energy resources (DERs) including three subsystems of an electricity subsystem, a CCHP subsystem and an auxiliary heating subsystem as the object of study in this paper. Besides, with the goals of reducing carbon emissions, increasing energy efficiency, and minimizing system cost, a constraint mechanism based on the DOM-PSO (dynamic object method/particle swarm optimization) algorithm was applied. Finally, taking Tianjin Eco-City as an example, we used the PSO algorithm to analyze the operating characteristics of the cold and power cogeneration system under the uncertainty of the wind power output. The simulation results show that the joint optimization mode operation strategy can balance the results of different optimization modes by increasing the robust coefficient of wind power. Of all scenarios examined, the CCHP system coupled with the ground-source heat pump and energy storage technology performed best.

To promote the utilization of distributed resources, this paper proposes a concept of a micro energy system (MES) and its core structure with energy production, conversion, and storage devices. In addition, the effect of demand–response on the operation of a MES is studied. Firstly, based on uncertainties of a MES, a probability distribution model is introduced. Secondly, with the objectives of maximizing operating revenue, and minimizing operational risk and carbon emissions, a multi-objective coordinated dispatching optimization model was constructed. To solve this model, this paper linearizes objective functions and constraints via fuzzy satisfaction theory, then establishes the input–output matrix of the model and calculates the optimal weight coefficients of different objective functions via the rough set method. Next, a comprehensive dispatching optimization model was built. Finally, data from a MES in Longgang commercial park, Shenzhen City, were introduced for a case study, and the results show that: (1) A MES can integrate different types of energy, such as wind, photovoltaics, and gas. A multi-energy cycle is achieved via energy conversion and storage devices, and different energy demands are satisfied. Demand–response from users in a MES achieves the optimization of source–load interaction. (2) The proposed model gives consideration to the multi-objectives of operating revenue, operational risk, and carbon emissions, and its optimal strategy is obtained by using the proposed solution algorithm. (3) Sensitivity analysis results showed that risks can be avoided, to varying degrees, via reasonable setting of confidence. Price-based demand–response and maximum total emission allowances can be used as indirect factors to influence the energy supply structure of a MES. In summary, the proposed model and solution algorithm are effective tools for different decision makers to conceive of dispatching strategies for different interests.

To alleviate the shortcomings of large-scale grid connections for clean energy, which require stable thermoelectric units to provide backup services, a stable cooperative alliance among different energy types of power sellers must be established. Consequently, a reasonable method to distribute income is required, due to different contributions of each entity in the alliance. Therefore, this paper constructs a comprehensive correction algorithm for income distribution using an improved Shapely value method. We analyze the operating mode of the power seller, and establish the net income calculation model under both independent and alliance operations. We then establish an alliance operation optimization model that considers the constraints of unit output, as well as the balance between supply and demand, with the goal of maximizing income. Finally, an industrial park in a province of northern China is taken as an example to verify the model’s practicability and effectiveness. The results show that the power sales alliance can effectively promote clean energy consumption. The maximum reduction in thermal power generation and CO₂ is 8510 MW and 684.515 tons, respectively. We apply the algorithm to income distribution and find that the thermal power seller’s income increased by ¥1,463,870, which enhances the stability of the alliance. Therefore, our income distributing optimization model guarantees the interests of each participant to the greatest extent, and serves as an important reference for income distribution.

Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.

This paper focuses on an optimal schedule for a micro energy grid considering the maximum total carbon emission allowance (MTEA). Firstly, the paper builds an energy devices operation model and demand response (DR) model. Secondly, to maximize the economical operation revenue, the basic scheduling model for the micro energy grid is constructed. Thirdly, the conditional value at risk method and robust stochastic theory are introduced to describe the uncertainty of wind power, photovoltaic power, and load, and a risk aversion model is proposed. Finally, this paper selects the Xinxiang active distribution network demonstration project in Jining, China as an example. The results show that: (1) a micro energy grid can make the most use of the complementary characters of different energy sources to meet different energy demands for electricity, heat, cold, and gas; (2) the risk aversion scheduling model can represent the influence of uncertainty variables in objective functions and constraints, and provide a basis for decision makers who have different attitudes; and (3) DR can smooth the energy load curves. MTEA can enhance the competitiveness of the clean energy market, thus promoting the grid-connected generation of clean energy. Therefore, the risk aversion model can maximize the economic benefits and provide a basis for decision makers while rationally controlling risks.