Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty

Considering energy sharing between independent market operators (IMOs) and multiple microgrids (MMGs), the energy trading and production problem for MMGs under uncertainty is addressed in this paper. A bilevel energy dispatch framework is established to coordinate energy sharing and production of MMGs. The energy trading of IMOs is formulated as a mixed integer quadratic programming (MIQP) in the outer level. The energy production of an individual MG is formulated as a robust optimization model in the inner level. Moreover, a hybrid algorithm including an analytical target cascading (ATC) algorithm, a column and constraint generation (C&CG) algorithm and diagonal quadratic approximation (DQA) method is developed to solve the proposed model. Finally, numerical results show the performance of energy trading strategy and the solution quality of the proposed approach.

Multi-level interactive unit commitment of regional power system

Low-carbon distribution system planning considering flexible support of zero-carbon energy station

Peer-to-peer (P2P) energy trading built on a smart information system in networked microgrids (MGs) is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed market urges the need for an efficient energy trading strategy that incentivizes self-interested MGs to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply and energy trading in a dynamic manner. Jointly considering the randomness of renewable energy generation, the time-dependent load demand, the operational constraints of ESSs and the distance-dependent energy transmission losses associated with energy exchange, the proposed energy control and trading mechanism minimizes the time average operational costs of individual MGs while reducing energy transmission losses within the system.

Network constrained economic dispatch of renewable energy and CHP based microgrids

This paper examines the numerical behavior of the analytical target cascading (ATC) for multilevel optimization of hierarchical systems based on the Augmented Lagrangian Penalty (ALP) formulation and four different solution strategies. The strategies considered include Augmented Lagrangian using the method of multipliers (AL) and alternating direction method of multipliers (AL-AD), Diagonal Quadratic Approximation method (DQA), and Truncated Diagonal Quadratic Approximation method (TDQA). Properties examined include computational cost or efficiency and solution accuracy based on the selected values for the different parameters that appear in each formulation. The different strategies are implemented using two- and three-level example problems. While the results show the interaction between the selected ATC formulation and the values of associated parameters, they clearly highlight the impact they could have on both the solution accuracy and efficiency.

6 - Energy trading and markets in microgrids

Networked microgrids (MGs) have a great potential to improve the efficiency, reliability, resilience, security, and sustainability of power supply services. Peer-to-peer (P2P) energy trading built on a smart information system in networked MGs is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed and competitive energy trading market urges the need for an efficient energy trading strategy that incentivizes the self-interested MGs with various energy production and consumption profiles to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables the MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply, and energy trading in a dynamic manner, jointly considering the randomness of renewable energy generation and load demand, operational constraints of ESSs and transmission losses associated with energy exchange. The proposed energy control and bidding algorithm allows each MG to dynamically and independently determine its energy control actions and price-quantity bids/offers, while the proposed trading pair matching algorithm matches the MGs on a many-to-many basis with respect to their individual payoffs, which couple price-quantity bids/offers of the MGs with distance-dependent energy transmission losses associated with energy exchange. Numerical simulation results demonstrate that the proposed distributed energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while reducing energy transmission losses within the system.

There are some problems in the traditional integrated energy market, such as single transaction mode, difficulty in effective dispatch, and lack of targeted subject to provide integrated energy market services. Therefore, this paper designs an integrated energy market service system based on the blockchain smart contract. The participants in the integrated energy market are divided into integrated energy suppliers, integrated energy users and integrated energy service providers. The integrated energy service providers set up energy trading centres and energy dispatch centres according to the needs of integrated energy suppliers and integrated energy users, and provide energy trading and dispatch services for them by using the Digital Currency Electronic Payment (DCEP) of the central bank and the blockchain smart contract. “Antchain” platform is used to simulate the energy trading and dispatch process, and the verifiable simulation results are given. It is proved that the integrated energy market service system based on the blockchain intelligent contract designed in this paper can effectively realize the security and convenience of energy trading and the efficiency and intelligence of energy dispatch.KeywordsIntegrated energy market serviceIntegrated energy service providerBlockchainDigital currency electronic payment

Cooperative energy transactions in micro and utility grids integrating energy storage systems

Decentralized optimal operation model for cooperative microgrids considering renewable energy uncertainties

Due to the intermittent production of renewable energy and the time-varying power demand, microgrids (MGs) can exchange energy with each other to enhance their operational performance and reduce their dependence on power plants. In this paper, we investigate the energy trading game in smart grids, in which each MG chooses its energy trading strategy with its connected MGs and power plants according to the energy generation model, the current battery level, the energy demand, and the energy trading history. The Nash equilibria of this game are provided, revealing the conditions under which the MGs can satisfy their energy demands by using local renewable energy generations. In a dynamic version of the game, a Q-learning based strategy is proposed for an MG to obtain the optimal energy trading strategy with other MGs and the energy plants without being aware of the future energy consumption model and the renewable generation of other MGs in the trading market. We apply the estimated renewable energy generation model of the MG and design a hotbooting technique to exploit the energy trading experiences in similar scenarios to initialize the quality values in the learning process to accelerate the convergence speed. The proposed hotbooting Q-learning based energy trading scheme significantly reduces the total energy that the MGs in the smart grid purchase from the power plant and improves the utility of the MG.

Microgrid (MG) cooperation, which allows information and energy exchange among multiple MGs, can improve the reliability of MGs and thus becomes a promising technique for multi-microgrid (MMG) systems. Since MG is generally selfish in nature, how to motivate MGs to participate in MG cooperation becomes an important issue. To do this, a Stackelberg game theoretic framework to MG cooperation, called the MG energy trading game (MGETG), is proposed. In the MGETG, an MMG center as the leader motivates and regulates the MG cooperation by setting the energy trading prices. Then, MGs as the followers determine their energy trading strategies so as to maximize their utilities. Nikaido-Isoda relaxation algorithm is applied to find the Nash equilibrium of MGs actions and a pricing algorithm is devised to find the energy trading price based on the golden section search. Our simulation results show that the proposed algorithms can improve the renewable utilization by 25.9% and reduce the total operation cost by 35.5% through MG energy trading.

With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply–demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.

As small-scale distributed energy is gradually expanding, commercialization of peer to peer(P2P) energy trading that freely exchanges energy among individuals in various countries is being commercialized, and the Microgrids (MGs) are considered to be an optimal platform for P2P energy trading. Although conducting electricity trade among individuals without going through power companies is still in its infancy, it is expected to expand gradually as the awareness of the shared economy grows and the MG spreads. Research on more efficient trading systems is needed while trading energy in MG. Therefore we propose a more efficient energy trading system that minimizes the loss in proportion to the distance of the power line when energy trading is performed in the MG. We have constructed a virtual MG environment and experimented with energy trading scenarios. As a result, when the algorithm is applied, loss in proportion to the distance is reduced by 2.495% and energy trading becomes more active. The amount of energy and the number of trades increased by 1.5 times during the energy trading process.