Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner's driving requirement, the charging cost of EV and thermal limits of cables and transformers in the proposed market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer's (EV owners) interests will centrally guarantee the EV owners' driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions.

Congestion problems might occur in distribution networks as the penetration of distributed energy resources (DERs) progresses. This study focuses on the complementarity of multiple energy resources in energy hubs (EHs) to solve possible distribution network congestions. First, we consider an EH in which combined cooling, heating and power (CCHP) units and heat pumps are integrated with renewable energy resources. The EH models the intrinsic coupling relationship among various energy carriers, forming a flexible operation where electricity, natural gas, cooling and heat can complement each other. Next, an optimal operation strategy of multiple energy hubs enables gas-to-electricity to provide local energy supply for EH during electricity peak periods, and consume renewable energy generation by the complementarity of electricity, heat and cooling. In addition, the uncertainty of renewable energy resources is taken into account via scenario-based stochastic programming. Numerical results show that the proposed operation strategy explores EHs' operation flexibility to mitigate distribution network congestion when we consider high renewable energy penetration in power systems.

Multi-energy complementary microgrids (MECMs) provide an important means to accommodate renewable energy sources due to their abundant adjustable resources and flexible operation modes. However, limited capacity and controllability are the main obstacles that prevent MECMs from participating in the market. In this study, we develop a transactive energy (TE) mechanism-supported energy sharing strategy to coordinate interconnected MECMs in a regional integrated energy system (RIES), where the uncertainty of renewable energy and loads is taken into account via stochastic programming. An RIES operator is introduced to trade with the utility grid as an intermediate player between the electricity market and MECMs. For the TE mechanism, we employ alternating direction method of multipliers (ADMM) algorithm to achieve distributed optimization of energy sharing, which is based on the average of the shared energy residual over all MECMs. A clear economic interpretation exists in the method, wherein shared electrical and thermal energy prices can be obtained. Case studies demonstrate the effectiveness of the multi-energy sharing scheme considering integrated demand response (IDR). Moreover, the distributed algorithm can be implemented and converge easily while respecting MECMs’ individual benefits and private information.

The interdependence of different energy forms and flexible energy interaction among multiagents in an integrated energy system (IES) are significant for reducing carbon emissions. Therefore, optimizing the IES to achieve low-carbon emission and economic goals is necessary. This study proposes an IES energy management method based on the energy–carbon integrated pricing method. First, a consumption-based integrated pricing model is proposed to calculate the energy–carbon integrated prices of electricity, thermal resources, and gas for energy service provider (ESP). Second, an energy management method based on the Stackelberg game is established, with the ESP as the leader and the prosumers as the followers. In the game model, the objectives of the ESP and prosumers are to maximize profit by formulating an appropriate energy–carbon integrated pricing strategy and maximize consumer surplus by optimizing load, respectively. Finally, the effectiveness of the proposed method is verified using practical examples. The results indicate that the proposed method can increase the profit of ESP and reduce carbon emissions more efficiently than traditional methods.

The future renewable-based power system will have an increased need for balancing power. Prosumers, having both generation and consumption capabilities, are expected to provide balancing power to the grid, if their flexibility can be appropriately managed. Meanwhile, undesirable line congestion and voltage violations may arise in the distribution network, when flexible resources respond to external control or price signals on a large scale. Hence, the development of an effective framework to coordinate flexibility at the distribution system level is of utmost importance. Such a framework should allow for an optimal provision of prosumer balancing power services within the boundaries of local network security constraints. In this study, a balancing market participation framework is proposed, adopting the concept of network-constrained transactive energy, to facilitate the interactions between the transmission system operator and aggregators who manage prosumer energy profiles. This framework retains user privacy and complies with the current market setup, where flexible energy is traded on the spot and balancing markets; however, it is ensured that the resulting energy profiles do not cause problems in the distribution network.