The multi-energy microgrid (MEMG) improves the energy supply economy through a multi-energy coupling operation. However, due to faults or maintenance, outages may occur in the main grid, forcing the MEMG switch to the islanded mode, which heavily impacts the energy supply reliability. To solve this problem, a multi-objective stochastic optimization approach for planning the MEMG considering unscheduled islanded operation is proposed in this paper. This method aims to optimize the planning decision, improve the operation economy in the grid-connected mode and enhance the energy supply reliability in the islanded mode. A planning-operation co-optimization model is established with two objective functions: the minimum total planning cost and the minimum expected economic loss caused by islanded operation. The model incorporates equipment capacity allocation constraints, operation constraints in two operation modes and load curtailment constraints in the islanded mode. In addition to considering the uncertainties of load and renewable energy, the model of the uncertain islanded mode is established using four correlated random variables, and the scenario tree is used for islanded scenario generation. The adaptive weighted sum (AWS) method is used to solve the multi-objective model and obtain an optimal and evenly distributed Pareto frontier. On this basis, the technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine the final planning scheme. Numerical results illustrate the effectiveness of the proposed method, which makes a trade-off between planning economy and energy supply reliability.

This paper proposes a bi-level optimization framework for buildings to heating grid (B2HG) integration in integrated heating/electricity community energy systems. The proposed B2HG framework enables buildings to carry out heating demand response to the integrated community energy systems (ICES). In upper level, ICES operator with energy conversion equipment devotes to maximize its profits by optimizing the energy generation/supply schedules and the sale heating price. In lower level, a detailed physical model of the building with adjustable indoor radiators is developed while considering the building thermal dynamics. Consumers minimize their heating costs by adjusting the flow rates of their radiators according to the heating price provided by the upper level. Then, the proposed bi-level optimization problem is converted into a mixed-integer linear programming using Karush-Kuhn-Tucker conditions and strong duality theorem. The piecewise linearization is used to eliminate the nonlinearity of heating network constraints. Numerical results show that consumers can provide heating demand response to ICES operator by considering the thermal dynamics. With the multiple energy conversion equipment, the ICES operator can unlock the flexibility from the supply side. Moreover, the proposed bi-level optimization method can combine the flexibility from both the supply and demand side, and benefits both the ICES operator and building consumers.

This paper presents a bilevel planning framework to coordinate truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to promote charging flexibility and provide more choices for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed at the upper level to optimize the location of charging stations. Furthermore, a collaborative capacity optimization (CCO) approach is formulated at the lower level to optimize the capacity of TMC and FC at candidate stations. In the proposed framework, origin-destination (OD) analysis, Floyd algorithm and Monte Carlo simulation (MCS) are employed to generate the spatial-temporal distribution of charging demand based on historical data. An improved income approach (IIA) is then developed to well capture the heterogeneity of EV users’ charging behavior. The waiting cost of EV users is estimated by their value of time (VOT), which helps them to make a better choice between TMC and FC. To solve the optimal model easily, the big-M method is applied to linearize and convert the nonlinear problem into a mixed-integer linear programming (MILP) model. Meanwhile, the analytical target cascading (ATC) technique is employed to realize the data exchange process between the upper and lower layers. Finally, numerical study demonstrates the effectiveness of the proposed framework and method.

Soft open points (SOPs) connected to distribution networks are instrumental in improving operational performance during outage periods, such as power supply reliability and power quality, and they have not been sufficiently studied. To fully harness the potential benefits of SOPs, a multiple-source distribution service restoration (DSR) strategy considering SOPs is proposed in this paper. The power outputs of distributed energy resources (DERs) and SOP flexibility are exploited to enhance the load restoration level and mitigate voltage deviations over a time horizon. Additionally, to cope with uncertainties, the chance-constrained method is adopted by the proposed strategy to allow intermittent renewable resources to participate in DSR. DSR is formulated as a mixed-integer linear programming (MILP) problem with many integer variables, making it very challenging to solve. To reduce the computational complexity, a two-stage accelerated algorithm (TSAA) is proposed. This allows us to solve a relaxed MILP problem in the first stage to decide the radial topology. An accelerated MILP problem is solved in the second stage to determine the load status as well as the outputs of the DERs and SOPs. Case studies on an IEEE 33-bus distribution network illustrate the effectiveness of the proposed DSR strategy and the feasibility of TSAA in reducing the computation time.

This paper proposes a joint electricity and carbon sharing framework with photovoltaic (PV) and energy storage system (ESS) for deep decarbonization, allowing distributed PV prosumers to participate in a sharing network established by aggregator of prosumers (AOP). The ESS-equipped AOP plays multiple roles as a carbon aggregator, an ESS operator, and an energy-sharing provider at the same time. First, a demand response (DR)-based model that incorporates the multi-strategy of ESS is proposed to optimize energy-carbon transaction. A low-carbon DR with consideration of electricity-carbon coupling is developed to incentivize prosumers to adjust consumption behavior for costs and emissions reduction. Second, a joint optimization based on Stackelberg game is proposed, where AOP is leader, and prosumers act as followers. A dynamic pricing mechanism is proposed for AOP to determine the electricity-carbon coupled selling and buying prices simultaneously. Meanwhile, prosumers would adjust their energy consumption as response to different sharing prices. In addition, a distributed optimization algorithm with interactions is used to reach the Stackelberg game equilibrium. Finally, through a practical testing case, the effectiveness of the method is validated in terms of economic benefits and PV sharing enhancement, as well as the reduction of carbon emissions.