To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ $\in$ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.

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.

In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into a multi-energy complementary system (MECS). Firstly, a scenario-generating technique is proposed for uncertainty factors using the Wasserstein method and the improved K-medoids theory. Then, a multi-objective model and solution algorithm are constructed under the objectives of attaining the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations. Meanwhile, the influence of different ESD operation modes on MECS operation is discussed, specifically, the longest life cycle (LLC) and the optimum economic efficiency (OEE). Thirdly, in order to solve the multi-objective model, a solution algorithm is proposed by using the rough set method to convert the multi-objective model into a single objective model based on the payoff table. Moreover, the complementary features of the MECS are evaluated in terms of the load tracking degree, HS secondary peaking capacity, and units of coal consumption. Finally, the improved IEEE 14-bus system is chosen for the simulation analysis. The results show that (1) the proposed uncertainty simulation method can efficiently generate the most representative scenarios; (2) MECSs can utilize complementary power sources, the OEE mode can better optimize MECS scheduling, and the LLC mode can ensure the ESDs’ life cycles; (3) the scheduling scheme of MECS operation reach the optimal level when the capacity ratio of ESD:WPP–PV iso [0.62, 0.77] in the OEE mode and [1, 1.08] in the LLC mode on a typical summer day, and the ratio is [0.92, 1] in the OEE mode and [1.23, 1.31] in the LLC mode on a typical winter day. Therefore, the proposed model provides effective decision-making support for designing the optimal plan for MECS operation.

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs.

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings.

The rural power system has the advantage of developing renewable energy. However, the regulating capacity for rural power system to deal with the uncertain renewable energy is mainly from the connection with the main power grid. Thus, we build the time series production simulation model to analyze the challenges for rural power system gradually running off the grid. We increase the capacity of energy storage and other generation types to reduce the load shedding when the regulation capacity provided by the main power grid is reduced. Simulation results show that the installations of different items have different effects to improve renewable energy accommodation and load shedding and those items influence each other in the promotion of the two aspects above.