• Energy Research
• 7. Clean energy close

The operational flexibility of electric power systems (EPS) is restricted by combined heat and power (CHP) units that act to maintain sufficient heating supply. By exploiting the pipeline heat storage property in central heating systems (CHS), combined heat and power dispatch (CHPD) can significantly increase the operational flexibility and reduce wind power curtailment. In this paper, pipeline heat storage is modeled in the CHPD model under the constant mass flow heating dispatch mode, and the CHPD model can be formulized as a quadratic programming problem. Since the EPS and CHS are independently operated by the EPS and CHS operators, a decentralized solution to the CHPD model is proposed. During each iteration of the decentralized procedure, an optimal cut or a feasible cut is generated by the CHS operator and sent to the EPS operator. A robust model considering the wind power uncertainty is also studied with the proposed decentralized solution. This decentralized solution has a high efficiency and a light communication burden. Numerical tests on practical systems demonstrate the feasibility of the proposed decentralized method and the economic benefits brought by reducing wind curtailment.

The dynamic dispatch (DD) of battery energy storage systems (BESSs) in microgrids integrated with volatile energy resources is essentially a multiperiod stochastic optimization problem (MSOP). Because the life span of a BESS is significantly affected by its charging and discharging behaviors, its lifecycle degradation costs should be incorporated into the DD model of BESSs, which makes it non-convex. In general, this MSOP is intractable. To solve this problem, we propose a reinforcement learning (RL) solution augmented with Monte-Carlo tree search (MCTS) and domain knowledge expressed as dispatching rules. In this solution, the Q-learning with function approximation is employed as the basic learning architecture that allows multistep bootstrapping and continuous policy learning. To improve the computation efficiency of randomized multistep simulations, we employed the MCTS to estimate the expected maximum action values. Moreover, we embedded a few dispatching rules in RL as probabilistic logics to reduce infeasible action explorations, which can improve the quality of the data-driven solution. Numerical test results show the proposed algorithm outperforms other baseline RL algorithms in all cases tested.

The coordinated solution of AC optimal power flow (ACOPF) in the integrated transmission and distribution grid utilizes controllable energy resources in distribution grids for introducing additional operational economy and security benefits of power grids. However, it is rather impractical to compute the ACOPF centrally considering the information privacy in the operation of transmission and distribution grids. Due to the large capacity of distributed generators integrated to distribution grids, existing decentralized methods may encounter numerical problems and fail to converge when nonlinear ACOPF models are applied. In this paper, we propose a decentralized method to solve the ACOPF for integrated transmission and distribution grids. The proposed ACOPF model is characterized through polar-coordinate equations and branch flow equations for transmission grids and distribution grids respectively. A distribution-cost-correction framework is developed for a fast-convergent solution which is based on approximated distribution cost functions. A rigorous proof is provided for convergence and numerical simulations are analyzed for standard IEEE systems which demonstrate the advantages of the coordinated approach in reducing generation dispatch costs and mitigating overvoltage problems. The paper validates through simulations that the accuracy, computational efficiency, and scalability of the proposed approach are superior to those of traditional methods.

This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback–Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a “min-max-min” programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this “min-max-min” DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy.

With the rapid growth of wind power penetration, system operators are required to enforce grid codes that implement frequency regulation of wind farms. This paper presents a distributed cooperation framework for a doubly fed induction generator-based wind farm to participate in primary frequency regulation, including imitated inertia and droop characteristics similar to conventional plants. Compared with centralized schemes, the control efficiency is significantly improved. To realize fast distributed coordination and optimal power distribution among wind turbines (WTs), a distributed Newton method is developed, which only requires WTs to exchange limited information with their neighbors over a sparse communication network, and has a super-linear convergence rate. By introducing an index of state of energy, this method can adequately exploit the kinetic energy of all WTs without loss of security. The simulation results indicate that the method exhibits satisfying dynamic performance and reliability, and the system frequency can be stabilized faster when the wind farm controlled by the proposed method participates in frequency control.

To capture the stochastic characteristics of renewable energy generation output, chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical reformulation for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenario-based methods are used to approximate probability with heavy computational burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-scale, park-scale, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

Factories for which steam is indispensable typically form an industrial park to share steam heating network (SHN) infrastructure. The energy systems in such parks are usually operated as microgrids. To improve operation efficiency, potential flexible and controllable resources should be utilized. This article proposes a novel method of utilizing the hydraulic inertia of SHNs to improve flexibility for these microgrids. A dynamic hydraulic model is established to characterize the steam storage property of SHNs. Based on the model, a joint dispatch problem formulated as a difference-of-convex problem is developed for the SHN and the electric power network (EPN). The convex-concave procedure method is employed to deal with the nonconvex constraints in the problem. Considering the EPN and the SHN are managed and operated independently by different entities, a modified generalized Benders decomposition (MGBD) algorithm is proposed to solve the problem in a decentralized manner for privacy preservation. Numerical results indicate that utilizing the hydraulic inertia of SHNs can reduce the total operating cost, the peak load of the tie transformer, and distributed renewable energy curtailment. The MGBD method outperforms the baseline methods in terms of convergence and efficiency.

Coordinated heat and power dispatch has attracted increasing attention due to its flexibility support for renewable energy accommodation. Different from most conventional studies that regard coordination as holistic optimization, in which power systems monopolize the additional payoff brought by coordination while heating systems suffer from higher heat supply costs, this work considers coordinated dispatch from a novel multi-party perspective to ensure the privacy protection, mutual benefit, and mutual trust of all participants. Based on the existing works that address the privacy issue well, we propose a transfer payment strategy to ensure that both heating systems and power systems benefit from coordinated dispatch. The calculation of transfer payments is performed through a parametric programming method, which discloses only the payoff variation and maintains the privacy of the payoff itself. Finally, a critical region tracking method is developed to confirm the authenticity of the information exchanged between power systems and heating systems, which prevents possible defrauding. Numerical tests indicate that our proposed approach achieves the mutual benefit of both systems in coordinated dispatch and detects inauthentic information effectively to ensure mutual trust.