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Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicate the supply-demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation-based resilience assessment algorithm of ADSs considering the microgrid formation based on grid-edge DERs. Microgrid formation is used to depict the resilience gain of grid-edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply-demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123-bus feeder validate the effectiveness of our proposed method.

Hydrogen has shown great potential in the renewable power integration and urban mobility decarbonization like the hydrogen fuel cell vehicles (HFCVs). The HFCV refueling as an essential hydrogen load is of great flexibility. Considering the HFCV demand response, this paper studies the integrated electric power and hydrogen system (IPHS) operation. First, the HFCV refueling load model is formulated with its routing on the transportation network considered. Second, the optimal IPHS operation model is developed in which the electric power operation, tube-trailer based hydrogen delivery and HFCV refueling are coordinated. Third, a Lagrangian Relaxation (LR) based method is developed to solve the proposed model efficiently, which corresponds to a price-based demand response mechanism for HFCVs. Compared with existing works on IPHS, the influence of transportation networks is delicately analyzed on both HFCV refueling and hydrogen delivery. The case studies have proven the effectiveness of the proposed method and demonstrated that the overall operation cost is decreased via the proper guidance of HFCV refueling. The HFCV demand response shows great potential in exploring the synergy of energy and transportation systems.

Rapid growth of gas-fired generators strengthens the interdependency of electricity and natural gas markets. To meet the uncertainty challenge, a robust day-ahead market clearing framework for the integrated electricity and natural gas system (IEGS) is presented. First, a robust security-constrained unit commitment (SCUC) model is built considering the joint dispatch of ancillary services. Regulation-up/down reserve is scheduled to balance electrical load/wind power uncertainties, spinning reserve is to manage N-1 generator outage, and natural gas reserve is to withstand gas load uncertainty and provide fuel for gas-fired electrical reserve. The SCUC model is a two-stage mixed-integer nonlinear optimization problem so that a sequential linear approximation-based Column & Constraint Generation (SLA-based C&CG) algorithm is proposed. Then, according to SCUC solutions, an economic dispatch model is derived to obtain market clearing and pricing results. Based on locational marginal pricing, novel definitions of electrical energy, natural gas and multiple ancillary service prices are proposed, which can reflect network limits and uncertainty effects. Numerical cases on two test IEGSs and a provincial-level IEGS demonstrate the high accuracy of proposed SLA-based C&CG algorithm and the impact of uncertainties on market clearing.

Power distribution systems are vulnerable to natural disasters and malicious attacks. An efficient and accurate online risk assessment tool is very necessary to provide timely warning information for emergency dispatch of resilient distribution systems. However, conventional analytical risk assessment methods are subject to known network information, while emerging datadriven methods rarely incorporate resilient resources into the risk assessment procedures, limiting their accuracies when applied to extreme events. To solve the problems, this paper proposes an improved online data-driven risk assessment method adaptive for resilient distribution systems. Twenty-five basic operational indexes from practical experience are chosen to indirectly reflect the system risk, and the complicated relationship between the indexes and risk is characterized by entropy weights and gray correlation degrees. The proposed method is validated on a modified 33-node system, and the results show that it has better accuracy compared with similar approaches in online risk assessment during extreme events. The whole scheme can be helpful for the software design and hardware layout of future resilient distribution systems.

This paper proposes an integrated electricity-thermal energy management system (EMS) for high-speed railways. First, an operational model is built for the integrated electricity-thermal system, including a train operation EMS (TO-EMS) model and station operation EMS (SO-EMS) model. In the TO-EMS model, traction grids (TGs) are formulated with a solvable second-order cone programming problem. In the SO-EMS model, station indoor thermal systems are taken into account, and the building heat exchange process and solar radiation influence upon station indoor temperature are also included. Then the TO-EMS and the SO-EMS are coordinated with an alternating direction method of the multipliers-based (ADMM-based) algorithm, protecting the privacy and interests both for the train dispatch center and stations. To demonstrate the effectiveness of the proposed railway EMS, a modified realistic high-speed railway segment with six stations in North China with summer and winter scenarios is studied.

Composed of multiple integrated energy systems (IESs) belonging to different stakeholders, multi‐agent IESs (MA‐IESs) are widely concerned because of data privacy protect. As the basis of planning design and reliability evaluation for MA‐IESs, distributed energy resource coordination (DERC) problem is studied in this paper. First, a DERC model for MA‐IESs is established, which considers energy conversion process specifically. Meanwhile, a novel distributed accelerated descent (DAD) algorithm is proposed to realize fully distributed solving. Different from most of the existing researches that investigate the DERC with box constraints, the presented algorithm is able to solve the DERC with general convex constraints. Moreover, the backward operators in the method improve the convergence rate to the best of distributed first‐order optimization algorithm with fixed step size, O(1/T). Furthermore, the presented approach is initialization robustness when the load fluctuations suddenly happened in MA‐IESs. The convergence property, computing, and communication complexity are strictly proved. Finally, the effectiveness of DERC model and DAD algorithm are demonstrated by some modified case studies.

An integrated energy system (IES) is a regional energy system incorporating distributed multi-energy systems to serve various energy demands such as electricity, heating, cooling, and gas. The reliability analysis plays a key role in guaranteeing the safety and adequacy of an IES. This paper aims to build a capacity reliability model of an IES. The multi-energy correlation in the IES can generate the dependent capacity outage states, which is the distinguished reliability feature of an IES from a generation system. To address this issue, this paper presents a novel analytical method to model the dependent multi-energy capacity outage states and their joint outage probabilities of an IES for its reliability assessment. To model the dependent multi-energy capacity outage states, a new multi-dimen-sional matrix method is presented in the capacity outage probability table (COPT) model of the generation system. Furthermore., a customized multi-dimensional discrete convolution algorithm is proposed to compute the reliability model, and the adequacy indices are calculated in an accurate and efficient way. Case studies demonstrate the correctness and efficiency of the proposed method. The capacity value of multi-energy conversion facilities is also quantified by the proposed method.

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation.

Reliability evaluation of power distribution systems is a well-studied and understood problem, but topology analysis efficiency and failure feature description are still limiting the practical application of reliability evaluation in distribution systems. One approach the authors proposed in this study is to incorporate the machine-learning technique into the simulation-based reliability evaluation method, and assess the system reliability in an empirical fashion. In this study, a framework for performing the machine-learning-based reliability evaluation, and corresponding modelling processes are first established. Then, by inducing a supervised learning algorithm named perceptron, a state-space classification-based (SSC) method for system state assessment, the core procedure of reliability evaluation, is proposed. On this basis, a reliability evaluation algorithm combining the SSC-based system state assessment and sequential Monte Carlo simulation is proposed, where the workload of topology analysis required in conventional reliability evaluation methods can be released. Furthermore, extensive case studies are conducted on the Roy Billinton Test System (RBTS) bus 2 system and an actual distribution system to verify the proposed models and algorithms. Results show that the proposed framework supports a more efficient reliability evaluation pattern while ensuring the evaluation accuracy.

The emerging multi-energy system has brought new challenges and opportunities to energy business worldwide. To address the issues in multi-energy trading, this paper proposes an electricity, heating, and cooling trading model for the interaction between the multi-energy service provider (MESP) and multi-energy consumer (MEC) by using bi-level programming. In the upper level, the model instructs the MESP to make decisions on the optimal energy purchasing scheme and energy economic dispatch. In the lower level, optimal consuming patterns of different energies with the given retail prices are modeled for the MEC. Specifically, a novel multi-energy demand response (DR) program that employs energy conversion devices is proposed. Numerical results show that MEC can reduce its consumption cost via the multi-energy DR. Meanwhile, MESP benefits greatly from the flexibilities of energy conversion. This research can provide theoretical support for the future development of multi-energy trading.

Flexibility tools like demand response, distributed storage and integration of other energy networks are all key aspects of efficient utilisation of the electrical grid. This study presents a complete model for the reliability-based planning of district integrated energy systems (IESs) considering distributed energy storage and integrated demand response. First, an optimal planning model of district IES is proposed to determine the type and capacity of new facilities to be invested over the planning horizon, for minimising total investment and operation costs. Furthermore, a joint N−1 and N−k reliability criterion is introduced to construct the complete reliability-based planning model for economical and reliable planning solutions. The proposed model is solved via a heuristic algorithm, by iteratively solving a base-case master problem and two operation subproblems to check N−1 and N−k criteria. Numerical case studies demonstrate the effectiveness of the proposed reliability-based planning model and methodology. Results show that the model can promote the flexibility of distribution systems while ensuring reliability and economy.