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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.

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.

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.

The surging electricity consumption and energy cost have become a primary concern in the planning of the upcoming 5G systems. The integration of distributed renewable energy sources (RESs), such as solar and wind, is considered to be a viable solution for cutting energy bills and greenhouse gas (GHG) emissions of 5G base stations (BSs). Meanwhile, battery swapping (BSW) service for electric two-wheelers (E2Ws) is a burgeoning method to address the issue of E2Ws refueling in urban areas. The authors spotted potentials in the integration and cooperation of 5G BSs, distributed RES generations, and BSW systems for E2Ws. This paper proposes a simulation-based optimization framework for cooperative planning of the integrated system of 5G BS, RES generations, and BSW systems. A multi-paradigm simulation model is presented to mimic all the heterogeneous components contained in the integrated system, as well as their complex interconnections, and to take the involved uncertainties and distributive characteristics into account. Then an optimization model is developed based on the presented simulation model under different scenarios, to minimize the comprehensive annualized expense, with consideration of all the operation and planning constraints of the integrated system. Finally, the proposed simulation-based optimization framework is applied to practical cases under different scenarios. Numerical results and comparison analysis reveal how the integration of RES generations and BSW systems benefit 5G BS in expense cutting and RES accommodating.

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.

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.

Power systems are vulnerable to natural disasters and a proper load restoration strategy after blackouts is significant for the resilience enhancement. Current studies of load restoration including transient constraints have not considered the dynamic boundaries of microgrids and the software interaction between steady-state optimization and transient simulation has not yet been fully expounded on. To solve these problems, this paper proposes a load restoration scheme utilizing dynamic microgrids formation with transient constraints guaranteed. Specifically, an online closed-loop of steady-state optimization and transient simulation is established. For practical applications, the interaction between PSCAD and MATLAB is also presented in detail. The case study based on the IEEE 37-node test feeder shows the importance of integrating transient simulation verification and feedback in the load restoration strategies. In addition, the proposed scheme can provide guidance for further hardware-in-the-loop test and industrial controller design.

For secure operation of integrated electricity and natural gas system (IEGS), reserve is a useful support to manage renewable uncertainties and N − 1 contingencies. Thus, a day-ahead economic dispatch model of IEGS with reserve scheduling is presented in this paper. Considering the uncertainty of gas flow direction, a novel second-order cone (SOC) relaxation of Weymouth equation is designed to address the nonconvexity. Then, the proposed robust nonconvex model is mathematically transformed into a solvable mixed integer second-order cone programming (MISOCP) problem. To guarantee the tightness of SOC relaxation and achieve accurate dispatch solutions, MISOCP results are corrected accordingly by the multi-slack-node gas flow calculation with the Newton–Raphson method. Numerical cases are performed on IEEE 39-bus-15-node and IEEE 118-bus-40-node test IEGSs, demonstrating the proposed approach is feasible and effective for exact IEGS day-ahead dispatch and the proposed MISOCP model can provide a more economic dispatch solution with a shorter computational time than conventional MISOCP and mixed integer linear programming (MILP) models.

With the coordination of various distributed energy resources, microgrid has the capability to participate energy and reserve market simultaneously via a microgrid aggregator (MGA). The profit-maximizing offering problem of MGA satisfying a pure strategy Nash equilibrium (NE) is modelled in the paper as a bi-level programming to analyze the impact of MGA on the market equilibrium. A linearization method for pure strategy NE conditions is proposed, and then the bi-level problem is equivalently transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucher (KKT) conditions and strong duality theorem. Numerical results demonstrate the effectiveness of proposed method and present market NE prices are influenced by the scalability and location of integrated MGA.

China is confronting with large amount of renewable energy curtailment, while the distributed renewable generation is in an explosive growth. To enhance the renewable energy utilization, China has embarked on reforms on distributed generation PV (DG PV) trading in the distributed energy market. This paper introduces the new reform that put forward three DG PV trading patterns –direct trading between DG PV and consumers, commissioned power grid sales and full generation purchased by the grid. We analyze the impact of DG PV trading reform on interests of stakeholders in the DG energy market. We argue that increasing integration of uncertain DG and efficient operation of DG energy market require higher technologies in storage, forecasting, smart monitoring for the grid to guarantee system reliability and security. Future roles of DG energy market are sketched out to integrate the market participants in a user-friendly and comprehensive way. This paper will be insightful to provide a framework for future Chinese DG energy market development and help it transit to an open retail electricity market in the future.

With the support of flexible gas-fired generations and power-to-gas technologies, the integrated electricity and natural gas system (IEGS) is able to achieve a deep penetration of renewable energy. This paper proposes a two-stage robust day-ahead dispatch model for IEGS against the uncertainty of wind power, electricity load and gas load. Nonlinear and nonconvex natural gas flow constraints are relaxed into the second-order cone constraints considering the gas flow directions. Then the Column &Constraint Generation method is adopted to solve the proposed two-stage robust optimization problem. An IEEE 39-bus-20-gas- node test IEGS is used to test the effectiveness of proposed model.

As high penetration of uncertain wind and solar energy into power system, spatio-temporal correlation of wind and solar energy is of great necessity for planning and operation of wind farms and PV plants. A Copula function and first order Markov process based Monte Carlo simulation method of multivariate wind speed and PV power spatio-temporal series is proposed in this paper. In the proposed approach, either spatial correlation or temporal correlation of wind speed and PV power series is considered including cross-correlation between all series and self-correlation of every series. The results of case study based on National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy are analyzed, which verify the scientific and feasibility of the proposed approach.

Following the fast development of smart grid, it is essential that appropriate pricing scheme is important to encourage customers to participate in the electricity market operation. Therefore, an optimal real-time pricing model based on demand response is presented in this paper. Finally, the IEEE 24-bus system is employed to verify the proposed model.

Increasingly frequent natural disasters and man-made malicious attacks threaten the power systems. Improving the resilience has become an inevitable requirement for the development of power systems. The importance assessment of components is of significance for resilience improvement, since it plays a crucial role in strengthening grid structure, designing restoration strategy, and improving resource allocation efficiency for disaster prevention and mitigation. This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms. Firstly, the component failure rate model under wind storms is established. According to the model, system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method. For each system state, an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching. The distribution functions of component repair moment can be obtained after a sufficient system state sampling. And Copeland ranking method is adopted to rank the component importance. Finally, the feasibility of the proposed approach is validated by extensive case studies.

Customer baseline load estimation is the basis of Demand Response(DR) for it decides the DR performance evaluation and financial settlement of customer participation. The accurate estimation of baseline load is hard because of complexity and uncertainty of customer behavior. Besides, baseline load is an ideal load which cannot be metered in reality. This paper proposed a data driven Fuzzy C Means(FCM) based baseline load estimation method. The fuzzy characteristics of customer electricity consumption behavior is adequately considered and the complex factors of customer behavior are implicitly considered. The proposed method has high accuracy compared to conventional methods.

The electrical, natural gas and heating subsystems of an integrated energy system (IES) can be managed by multiple independent energy operators (participants), individually. In the face of contingency events, it is essential to coordinate these participants, who pursue their own profits, in order to optimize the post-contingency dispatch of the IES and reduce the system loss. Therefore, this paper proposes a novel coordinated post-contingency dispatch scheme for the IES with multiple participants. Firstly, an emergency energy market framework of the IES is developed based on a distributed energy trading mechanism, to coordinate the energy allocation among different participants during contingencies. Then, the post-contingency dispatch model of the IES is formulated to maximize the total social welfare of the IES during contingencies. Moreover, a distributed optimization algorithm for the IES is developed based on the alternating direction method of multipliers (ADMM) with self-adaptive penalty parameters, which provides the optimal emergency energy prices to effectively coordinate the decision-making process of participants. Finally, through case studies, the proposed method is compared to the traditional centralized dispatch method and decentralized method based on regulated energy prices to demonstrate the effectiveness of the proposed method.

Although the transportation tends to serve as flexible loads to smooth the energy demand profile, the transportation capacity is far from fully exploited to promote the efficiency of energy systems. Railways can transport a large volume of liquefied natural gas (LNG) to natural gas networks, providing an alternative to pipelines. Motivated by this idea, this paper interconnects integrated electricity-gas energy systems (IEGES) with double-track freight railway networks (DTFRN) and proposes a joint economic dispatch model for integrated railway-electricity-gas systems (IREGS) considering railway operation requirements. The model is formulated into a solvable mixed integer linear programming by applying the piecewise linear approximation method on Weymouth equation. Simulations tested on a modified IREGS system proves the effectiveness of the dispatch model. The results show that the DTFRN is of benefit in reducing the amount of compressor-consumed gas and enhancing the gas peaking capacity, and therefore it lowers the operation cost of the IREGS.

Integrated energy systems (IES) are the best way to handle environmental concern and energy crisis. But it can also make energy systems more vulnerable. What's worse, the extreme events such as natural disasters and malicious man-attack become more frequent, which bring huge challenge to IES. Improving the resilience has become an inevitable requirement for the development of IES. In this paper, a resilience based hardening approach for integrated power and natural gas systems is presented, this approach can determine where to install gas generators and gas storages to improve resilience. The effects of the extreme events are discussed because multi-failure scenarios are considered when it comes to resilience. The stochastic optimization model of integrated power and natural gas systems (IPGS) is established. The piecewise linearization method is adopted for nonlinearity. The Monte Carlo method and progressive hedging algorithm are adopted to improve the solving efficiency. A modified IES is adopted to demonstrate the validity of the proposed approach.

Integrated electricity and gas energy systems are typical integrated energy systems (IES). In order to improve the resilience of IES in extreme events, an optimal placement method of gas turbines and gas storage systems in integrated electricity and gas energy systems considering natural disasters and man-made attacks is proposed in this paper. After natural disaster scenarios are generated, a stochastic defender-attacker-defender (DAD) model is built. Improved nested column & constraint generation algorithm (NCCG) and progressive hedging algorithm (PHA) are adopted to solve the model. With the minimax-regret criterion, optimal placement scheme is determined. Finally, the effectiveness of the presented method is demonstrated.

With the deep interaction of power and natural gas systems, the security-constrained optimal power and gas flow (SCOPGF) is pivotal to ensure a safe operation of the integrated electricity and natural gas system (IEGS). Considering that post-contingency corrective control actions will make the final operation point obtained from SCOPGF more robust to potential contingencies, this paper studies the effectiveness of distributed energy storage (DES) on the IEGS short-term ( 15min) corrective actions for N-1 contingencies. Considering the gas storage effects of gas pipeline, linepack is modeled for long-term corrective actions. A greedy algorithm is proposed to handle the numerous N-1 contingency computation disasters. Test results demonstrate that the dispatching of DES can alleviate the energy transmission congestion during the emergency periods and reduce operating costs of the system.

Coastal distribution systems are vulnerable to typhoon weather. An efficient and accurate real-time risk assessment is very necessary in providing timely warning information for decision making. However, the conventional risk assessment methods are subject to varied typology, parameters and uncertainties. While in current data-driven methods, the effect of complex factors such as resilient resources and emergency response are difficult to be analytically incorporated in risk assessment procedure. To solve the problems, this paper proposes an improved data-driven real-time risk assessment method for resilient distribution systems during typhoon weather. A comprehensive risk-oriented database with 25 indexes is constructed based on practical experience in system operation. And then the effects of various complex factors on distribution system risk are characterized by entropy weights and gray correlation degrees. The case study on a modified 33-node system has validated the proposed method in real-time risk assessment. The whole scheme can be helpful for the software and hardware update of resilient distribution systems.

The electrified railway system with liquefied natural gas (LNG) transported is closely related to the integrated electricity-gas energy system, but little research focuses on the coordinated optimal operation of these systems. To fill the gap, this paper innovatively proposes the co-optimization scheduling model of electricity and natural gas systems considering railway transportation. Firstly, a railway network model for LNG transmission is put forward. Then a co-optimization scheduling model for the integrated electricity-natural gas system and railway system is established. Finally, the incremental linear approximation is adopted to transform the original model into a mixed integer linear programming model. The results of case studies show that the proposed co-optimization scheduling model is beneficial to reduce operating costs and promote the accommodation of renewable energy.

The coal transportation is not well coordinated with power system, resulting in increased electricity generation cost. Thus, need for joint operation of electricity system and railway system arises. This paper addresses two problems. First, a railway system model comprised of station and track is proposed, with single track constraints included. Second, a mid-term dispatch model of integrated electricity and railway system (IERS) is established with the consideration of unit commitment and N-l contingencies. Based on the proposed model, operation cost of IERS can be optimized. Besides, dispatch results of power system, as well as operating strategies for railway system, namely train timetable including trains’ path and coal cargo, can be obtained. Extensive numerical cases are performed on a modified IEEE 39-bus and a 14-node railway network to illustrate the effectiveness of the proposed model, demonstrating that the model can ensure coal supply and decrease the operation cost of IERS.

With the significant development of liquefied natural gas (LNG) rail transport, the railway system is increasingly more closely connected with the integrated electricity-natural gas system (IEGS). To coordinate the economic operations of the two systems, this paper innovatively proposes a coordinated dispatch model of IEGS with LNG infrastructures and a freight railway network with LNG transport. First, an operational scheduling model of the railway network, considering energy consumption, is put forward for both LNG transmission and ordinary freight transport. Then, the coordinated dispatch problem of IEGS and the railway network is formulated into a mixed-integer linear programming model via the big M method and a modified incremental linearization approach. Finally, a bi-level optimization algorithm based on generalized benders decomposition (GBD) is presented to solve the coordinated dispatch problem due to the restrictions on exchanging private information. Case studies demonstrate the effectiveness of the proposed model and algorithm as well as the potential benefit for wind power accommodation.

In 2016, solar and wind energy production were greatly curtailed in China, especially in its northwestern region. This paper identifies administrative factors and market barriers as the main causes of renewable curtailment. In 2017, the Northwest Power Grid (NWPG) successfully implemented a variety of approaches to reduce wind and solar curtailment. In this paper, we analyze the change in power generation, expansion of renewable energy delivery, and electricity-replacement projects across provinces that occurred in 2017. While acknowledging the practical results of the NWPG's efforts, we identify challenges that the northwest still faces in integrating renewable energy. We argue that trans-regional administrative barriers are preventing the effective utilization of renewable energy nationwide. To address those barriers, we explore market-based mechanisms for promoting the integration of large-scale renewable energy. In addition to long-term contracts for renewable energy across provinces, both spot-market and the ancillary service market would facilitate the uptake of renewable energy. In addition, price signals should be used to guide peak shaving. Market mechanisms will be insightful to dissolve barriers and enhance utilization of renewable energy throughout the country.

As the rapid development of natural-gas fired units (NGUs), power systems begin to rely more on a natural gas system to supply the primary fuel. On the other hand, natural gas system contingency might cause the nonavailability of NGUs and inevitably jeopardize power system security. To address this issue, this paper studies security-constrained joint expansion planning problems for this combined energy system. We develop a computationally efficient mixed-integer linear programming (MILP) approach that simultaneously considers N-1 contingency in both natural gas system and electricity power system. To reduce the combinatorial search space of MILP models, an extension of a reduced disjunctive model is proposed to decrease the numbers of binary and continuous variables as well as constraints. The involving nonlinear terms in N-1 constraints are exactly linearized without sacrificing any optimality. Numerical results on two typical integrated energy systems demonstrate the necessity of extending N-1 criterion to the whole network of a combined energy system. Experimental results also show that compared with the conventional approach, our proposed MILP approach achieves a great computational performance improvement in solving security-constrained co-optimization expansion planning problems.

As the sharp growth of gas-fired power plants and the new emergence of power-to-gas (PtG) technology intensify the interdependence between electricity and natural gas systems, it is imperative to co-optimize the two systems for improving the overall efficiency. This paper presents a long-term robust co-optimization planning model for interdependent systems, for minimizing total investment and operation costs. Beside generators, transmission lines, gas suppliers, and pipelines, PtGs and gas compressor stations are also considered as investment candidates to effectively handle wind power uncertainties in the power system and compensate pressure losses in the gas network. Furthermore, the proposed model includes a joint N-1 and probabilistic reliability criterion to promote economical and reliable planning solutions. The proposed model is solved via a decomposition approach, by iteratively solving a base-case master problem and two operation subproblems to check N-1 and probabilistic reliability criteria. Numerical case studies illustrate the effectiveness of the proposed robust co-optimization planning approach.

There is an increasing awareness of resilience in the face of extreme natural disasters and man-made attacks. Power system resilience includes hardening measures and operational restoration measures. These two aspects of resilience measures are innovatively combined in this paper to improve power system resilience. A tri-level defender-attacker-defender (DAD) model is built to find the best hardening plan under malicious attacks given the available defending resources and operational restoration measures for a distribution system. On the first level, the system defender makes hardening decisions. On the second level, the attacker finds the attack scenario with maximum damage. Given the hardening and attack plans, on the third level, the system operator undertakes resilient operational measures including optimal DG islanding formation and topology reconfiguration. This new model overcomes the limitations of existing DAD models by integrating these operational measures into the mathematical model; the produced hardening strategy is more practical for today’s fast-developing smart distribution system. The DAD problem is formed as a tri-level mixed integer optimization problem and the Column Constraint Generation (CCG) method is adopted to accurately achieve the optimal solution. The proposed methodology is applied on an IEEE 33-bus system and a real 94-bus system. Its effectiveness is verified by numerical case studies.

As increasing penetration of renewable energy, stochastic power flow method becomes an essential tool to analyze the randomness and variation in power system. Conventional methods cannot count in the correlation between input random variables, which will lead to obvious error in most cases. Consequently, it’s necessary to study enhanced methods, and also conduct comparisons among them to provide criteria for method selection. This paper builds up the basic frame of stochastic power flow methods considering correlation and compares them, especially two typical cumulant methods in aspects of accuracy, efficiency and complexity. Useful conclusions are obtained for further study and application.

This paper proposes a novel approach to evaluate the renewable energy accommodation capacity (REAC) of power system with wind power and photovoltaic (PV) integration. Based on Copula theory, the wind power output model is proposed considering uncertainty and the spatial correlation of wind speed in different wind farms. The PV output model is presented based on solar irradiance and weather influence factor. The solar irradiance depicts the periodicity and correlation of PV output while the new concept of weather influence factor is introduced to reflect the uncertainty of PV output. And the REAC evaluation model considering reliability of power system with wind power and PV integration is proposed with Monte-Carlo method. The proposed method comprehensively considers the characteristics of renewable energy and evaluates power system REAC more accurately. Furthermore, the influences of multiple factors on REAC of power system are analyzed. Results of case studies based on IEEE Case 39 test system and actual historical renewable energy data of northern China illustrate the feasibility of proposed approach.

Increased penetration of renewable resources emphasizes opportunities for virtual energy storage (VES) to offer the needed flexibility to the power system. The VES, quite common in China, is in fact the large electricity industrial consumers with a thermal source of electricity generation that allows them to self-supply. The emerging VES concept breaks through the traditional self-supply off-grid status and enables them to interact with the grid as storage facilities by modulating their power source and load in bi-direction. Within this context, this paper analyzes the implications of the strategic participation of a price-making VES with the ability to exercise market power in the electricity market. We develop a stochastic bi-level optimization model, which presents the VES profit-maximization problem, subject to a day-ahead energy clearing optimization under renewable uncertainty. An all-scenario-feasible stochastic (ASFS) method is applied for the first time to the bid-offer problem to deal with the uncertainty, guaranteeing the feasibility of decision making for all scenarios. The proposed stochastic decision-making model is evaluated by using two illustrative test systems and a practical case based on Gansu Province, China. Numerical results reveal the various abilities of the VES merchant in different operating modes in manipulating the market power and gaining profits from price arbitrage under uncertainty. Our proposed model and approach provide an insight for the market operator to track the behaviors of the VES into the market clearing process and to evaluate the portfolio value of VES in spatio-temporal coordination of electricity production.

This paper presents a bilevel optimization model for the risk assessment of transmission systems. Specifically, the lower level model is expected to provide a generation redispatch by minimizing the total load shedding, and the upper-level model is to maximize the severity risk by constructing a binary optimization model to identify the worst N - k contingency. To further reduce the complexity of the proposed model, the logarithmic transformation and linearization techniques are utilized, leading to a general mixed integer linear programming. In addition, a recursive method is proposed for contingency ranking based on the bilevel optimization model. Compared with the benchmark analytic method, the proposed method does not need to enumerate every contingency, reducing the computational burden. In contrast to the traditional Monte Carlo Simulation method, the proposed method can give as precise a risk assessment result as the analytic method and has higher evaluation efficiency. Numerical results on three test systems verify the effectiveness of the proposed method.

Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of electric distribution grids. Microgrids with various types of distributed generators (DGs) have the potential to enhance the electricity supply continuity and thus facilitate resilient distribution grids under natural disasters. In this paper, a novel load restoration optimization model is proposed to coordinate topology reconfiguration and microgrid formation while satisfying a variety of operational constraints. The proposed method exploits benefits of operational flexibility provided by grid modernization to enable more critical load pickup. Specifically, a mixed-integer second order cone programming is employed to reduce the computational complexity of the proposed optimization with optimality guaranteed. Finally, the effectiveness of the proposed method has been verified on an IEEE 33-bus test case and a modified 615-bus test system.

The electricity infrastructure is a critical lifeline system and of utmost importance to our daily lives. Power system resilience characterizes the ability to resist, adapt to, and timely recover from disruptions. The resilient power system is intended to cope with low probability, high risk extreme events including extreme natural disasters and man-made attacks. With an increasing awareness of such threats, the resilience of power systems has become a top priority for many countries. Facing the pressing urgency for resilience studies, the objective of this paper is to investigate the resilience of power systems. It summarizes practices taken by governments, utilities, and researchers to increase power system resilience. Based on a thorough review on the existing metrics system and evaluation methodologies, we present the concept, metrics, and a quantitative framework for power system resilience evaluation. Then, system hardening strategies and smart grid technologies as means to increase system resilience are discussed, with an emphasis on the new technologies such as topology reconfiguration, microgrids, and distribution automation; to illustrate how to increase system resilience against extreme events, we propose a load restoration framework based on smart distribution technology. The proposed method is applied on two test systems to validify its effectiveness. In the end, challenges to the power system resilience are discussed, including extreme event modeling, practical barriers, interdependence with other critical infrastructures, etc.

Power system islanding operation acts as an important attractive corrective measure that is widely studied. However, a proper splitting strategy for the islanding operation should satisfy both steady-state and transient stability constraints. As a result, an ac power flow-based optimization model with dynamic simulation is proposed to obtain a proper splitting strategy. Specifically, the method includes two steps: 1) optimization of a splitting strategy satisfying the steady-state constraints and 2) transient stability evaluation of the strategy by dynamic simulation. Furthermore, the optimization and evaluation steps are iterated until finding a splitting strategy that satisfies both the steady-state constraints and the transient stability constraints. Finally, the effectiveness of the proposed method is verified on the WSCC 9-bus and the IEEE RTS 24-bus test systems.

Generation units with multiple fuel options and valve-point loading effects on the generators are fundamental parts of power system generation. However, economic dispatch (ED) of these units have non-convex, non-continuous generation cost functions. In this paper, a new Lightning Flash Algorithm (LFA) is proposed to solve complex non-convex ED problems in large scale power systems considering multiple fuel options and valve point effects on the generators. Five case study systems including 10-units, 40-units, 80-units, 160-units and 640-units are conducted to validate the applicability and effectiveness of the proposed LFA method for solving ED problems. The results are compared with other published methods in literature and confirm the applicability and effectiveness of LFA against other existing methods. LFA can successfully conduct the best fuel types of the generators and adjust the optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using proposed LFA method can minimize the total generation costs and optimally satisfy the load demands in the power grid.

The natural gas system and electricity system are coupled tightly by gas turbines in an integrated energy system. The uncertainties of one system will not only threaten its own safe operation but also be likely to have a significant impact on the other. Therefore, it is necessary to study the variation of state variables when random fluctuations emerge in the coupled system. In this paper, a multi-slack-bus model is proposed to calculate the power and gas flow in the coupled system. A unified probabilistic power and gas flow calculation, in which the cumulant method and Gram–Charlier expansion are applied, is first presented to obtain the distribution of state variables after considering the effects of uncertain factors. When the variation range of random factors is too large, a new method of piecewise linearization is put forward to achieve a better fitting precision of probability distribution. Compared to the Monte Carlo method, the proposed method can reduce computation time greatly while reaching a satisfactory accuracy. The validity of the proposed methods is verified in a coupled system that consists of a 15-node natural gas system and the IEEE case24 power system.

Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage.

As a regional power grid with the highest penetration of renewable energy, the China Northwest Power Grid is now facing with high curtailment of renewable energy, caused by the inefficient energy utilization, the serious marketization barriers between provinces and the immature market mechanism. To address these problems, this paper proposes an electricity energy pool (EEP) trading applied to the real-world case in China that is on the marketization process. Firstly, the EEP trading model is established, based on the economic dispatch model in two provinces whose power resources are complementary. Secondly, the price mechanism of the EEP is made up of two parts: system marginal price calculation and settlement of the unbalanced electricity (UE) within the whole month. Then, the benefit of the EEP trading is presented by comparing the rate of the curtailment in the wind and solar generation whether there is the EEP trading between the Shaanxi Power Grid and the Gansu Power Grid from day to month to year. Finally, the monthly UE of the EEP trading and its price are calculated, which verifies the effectiveness of the EEP trading and its price mechanism.

Operation and optimization of integrated energy systems represented by industrial parks are an important research hotspot in recent years, and have produced a large number of research results. The integrated energy system represented by industrial parks is of great significance for improving load utilization efficiency and reducing industrial production costs. This paper first introduces a data fusion method. Based on this, the fusion data is classified by the clustering method, and the results are evaluated by combining relevant indicators. Finally, this paper uses the daily electricity load and daily heat load data of industrial parks in Jiangsu Province as an example to analyze the coupling relationship between electric heating loads. This method facilitates industrial parks to develop strategies to reduce energy costs and increase production efficiency.

The deepening penetration of renewable energy resources poses critical challenges on the secure and reliable operation of power systems. Energy storage, which could smooth out the fluctuations brought about by the renewables, is one major solution to addressing those challenges. Storage planning, which determines the optimal site and size of the energy storage system, could effectively facilitate the integration of renewables. To consider the impacts of uncertain renewables, this paper proposes a probabilistic framework for storage planning. It explicitly models the operation risk by incorporating individual chance constraints. The chance constraints are converted to deterministic constraints using historical data via the scenario approach. The probabilistic storage planning problem is further decomposed into an upper- level problem and several lower-level problems, and solved via a sub-gradient cutting-plane algorithm. Numerical simulations are conducted on the IEEE 6-bus system.

Spot market plays a key role in putting forward the construction of electricity market and improving operation efficiency and security. A low-consumption and high-efficiency electricity market is proposed to fulfill geographical representation, energy saving and environmental protection. Construction of the spot market is described to consider the main features of the local region and solve the prominent problems of electricity market. Based on the experience of domestic and international power market, the framework of spot market is proposed to promote the renewable energy consumption for the power grid with high proportion of renewable energy.

In the context of energy transformation, the process of power system reform has been further accelerated. As an important part, the power spot market can achieve power balance in a smaller time scale, thus promoting the further optimization of resources. This paper established a clearing model for the day-ahead electricity market, simulated the complete day-ahead clearing process, explored the law of market operation through the analysis of examples, and discusses the influence of various market factors on the clearing results, including different market mechanisms, access to renewable energy, etc.

With higher penetration of renewable energy sources (RES), more combined-cycle gas turbines (CCGTs) will be deployed. These flexible gas-fired generators can provide efficient system reserve to deal with rapid power fluctuation of renewable energy. Therefore, an accurate reserve allocation (RA) on CCGTs is necessary to guarantee efficiency and safety of integrated electricity and natural gas system operation. Traditional RA model ignores the non-dispatchable start-up and shut-down processes of combustion turbines and steam turbines (i.e. transition trajectories) in CCGTs and is less considered about the effect from natural gas system. A novel RA model is proposed considering the effect of transition trajectories of CCGTs, gas-electricity coupling and unit commitment to meet the demand of load and RES forecast error and N-1 contingencies. The simulation results show that transition trajectories and gas load level greatly affect the reserve scheduling of CCGTs.

DC grid, which is more suitable for the integration of intermittent energy, should undergo a reliability evaluation before being put into production. This paper proposes the reliability model for AC-DC converter and DC-DC converter firstly. Then, a minimum load shedding model considering the interaction between AC grid, HVDC and DC grid is presented. Finally, based on the state enumeration, the reliability evaluation for the AC-DC hybrid grid is accomplished. The results show that moderate redundant SM and the reduction of IGBT and capacitance failure rate can significantly improve the reliability of the AC-DC hybrid gird.

Recently electric vehicles (EVs) have been greatly promoted and employed in the whole society. EVs are classified into four categories according to distinct driving characteristics and charging requirements in this paper. The price elasticity of EV charging demand is taken into account by introducing demand function according to microeconomics. Besides, the EV parking lot operator design the limit of the charging price depending on its profit requirements and EV user acceptance. Then the optimum charging price determination and energy management of EV parking lot is investigated through an optimization model aiming at profit maximization. To validate the proposed model, a case study is conducted and the results are analyzed to draw the conclusions.

To overcome the difficulties caused by the uncertainty of renewable energy in solving the unit commitment in integrated electricity and natural gas system (IEGS), a robust optimization model is proposed in this paper, in which the dynamic and gas storage tanks of natural gas systems (NGS) are also considered. The time scales of power system (PS) and the NGS are different from each other, and the dynamic characteristics of NGS are remarkably slower than those of PS, so it is necessary to model the dynamic NGS and explore its benefits for the PS. Moreover, since the gas storage tanks can cope with the stochastic fluctuations caused by the gas load, it is necessary to consider the benefits of the gas storage tanks in IEGS. The model is applied to the IEEE 39-bus-15-node IEGS to verify its validity.

In order to improve the utilization of renewable energy and reduce the damage caused by the volatility of renewable energy, it is necessary to further study the multi-energy combined operation strategy. In this paper, a combined operation mode of wind power, gas-fired power and pumped storage is put forward. In this mode, wind power is the main source while gas-fired power and pumped storage act as an adjustment to smooth wind power output. The model takes the maximum benefit of joint operation as the objective and adopts mixed integer linear programming. Through an example, the economic benefit of the combined operation mode of wind-gas-pumped storage is calculated. The results show that this operation mode not only improves the utilization of wind power, but also improves the efficiency of power grid operation.