[en] Highlights: • Developed a multi-objective model for the combined natural gas network and electricity network. • Taken into account the uncertainty and correlations of wind power in the proposed model. • Presented an improved point-estimation method to solve the combined optimal power and natural gas load flow. - Abstract: With the increasing proportion of natural gas in power generation, natural gas network and electricity network are closely coupled. Therefore, planning of any individual system regardless of such interdependence will increase the total cost of the whole combined systems. Therefore, a multi-objective optimization model for the combined gas and electricity network planning is presented in this work. To be specific, the objectives of the proposed model are to minimize both investment cost and production cost of the combined system while taking into account the N−1 network security criterion. Moreover, the stochastic nature of wind power generation is addressed in the proposed model. Consequently, it leads to a mixed integer non-linear, multi-objective, stochastic programming problem. To solve this complex model, the Elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to capture the optimal Pareto front, wherein the Primal–Dual Interior-Point (PDIP) method combined with the point-estimate method is adopted to evaluate the objective functions. In addition, decision makers can use a fuzzy decision making approach based on their preference to select the final optimal solution from the optimal Pareto front. The effectiveness of the proposed model and method are validated on a modified IEEE 24-bus electricity network integrated with a 15-node natural gas system as well as a real-world system of Hainan province.

[en] Highlights: • Analyzing zonal reserve requirements for wind integrated power system. • Modeling day-ahead optimal dispatch solved by chance constrained programming theory. • Determining optimal zonal reserve demand with minimum confidence interval. • Analyzing numerical results on test and large-scale real-life power systems. - Abstract: 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.

[en] Highlights: • Established a reliability evaluation model for multi-agent integrated energy system. • Proposed an optimal re-dispatching model to coordinate operation of each agent. • Presented a reliability evaluation approach with fully distribution communication. • Validated and analyzed the presented models and approaches in modified cases. The reliability evaluation of integrEated energy systems becomes very important, because it is the basis of planning and operation. However, different stakeholders in integrated energy systems bring difficulty to evaluation process due to data privacy protect. In this paper, a reliability evaluation approach for multi-agent integrated energy systems via fully distributed communication is presented. Firstly, a reliability evaluation model of multi-agent integrated energy systems is established, where wind turbines, plug-in electric vehicles and gas storages are considered in the structure. Moreover, a re-dispatching model with voltage and gas pressure constraints is presented to minimize operation cost, when a contingent incident happens. Through second-order cone and Big M relaxation, the re-dispatching model is reformulated to a solvable one. Furthermore, a state assessment method with fully distributed communication is proposed to reduce information exchange, which is presented based on Monte Carlo simulation and alternating direction method of multipliers with Gaussian back substitution. Reliability indices of each agent and whole system can be calculated via a little non-privacy message transferring. Finally, the proposed model and approach are tested on a modified MA-IES. The relevant failure among different sub-systems is analyzed and the effectiveness of fully distributed communication is validated.

[en] Highlights: • Proposed a fast reliability evaluation method for integrated power-gas system (IPGS). • Presented a novel consequence analysis model of IPGS failures based on graph theory. • Proposed a tailor-made importance sampling algorithm for IPGS reliability evaluation. • Reliability indices are developed in the aspects of system, customers and components. The supply reliability is a vital concern in the planning of integrated power-gas systems (IPGS). Previous reliability evaluation approaches of IPGS bring massive computational burdens due to the complex consequence (status) analysis model and numerous status samples in Monte Carlo simulation (MCS). In this paper, a systematic assessment approach is proposed to evaluate the supply reliability of IPGS rapidly. Firstly, a novel optimal load shedding model of IPGS is presented based on the stochastic capacity network model of gas system and the power flow model, which reduces the computational complexity of consequence analysis. Then, a tailor-made importance sampling (IS) method based on cross-entropy is proposed for IPGS to improve the efficiency of MCS. Through evaluating the criticality of training samples, the IS method accordingly alters the unavailability parameters of electricity and gas components, so that crucial risk events of IPGS are sampled more frequently in MCS. Furthermore, reliability indices of IPGS are developed in three hierarchies: system reliability, customer availability and component importance, which provide comprehensive references for system planners. Finally, numerical simulations are performed on two IPGS cases and the results validate the proposed approach significantly improves the computational efficiency of supply reliability evaluation for IPGS.

[en] 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.

[en] Highlights: • A co-optimization planning model of IEHS is proposed to obtain economic energy system. • District heating systems are modeled by hydraulic and thermal conditions precisely. • A novel approximation method is proposed to address highly nonconvex planning model. • A sequential bound-tightening method is used to reduce the approximation errors. • A parallel Benders decomposition is used to solve the planning model efficiently. The rapid growth of combined heat and power (CHP) units has led to the development of integrated electricity and district heating systems (IEHS). To support the design of a highly efficient energy supply system, this paper proposes a long-term co-optimization planning model for an IEHS. Not only CHP units, non-CHP thermal generators, wind farms and electric boilers but also transmission lines and heat pipelines are considered as investment candidates to meet electricity and heat demands. Nonlinear hydraulic conditions and thermal conditions are adopted to precisely capture the characteristics of the heating system. To make the planning model tractable, the nonlinear hydraulic conditions are approximated through piecewise linearization. Based on the introduction of auxiliary variables, the nonconvex thermal conditions are reformulated into linear constraints through quadratic convex relaxation. Hence, the planning model is converted into a large-scale mixed integer linear programming (MILP) problem. Since the planning model is formulated based on independent load blocks, a parallel Benders decomposition algorithm combined with the sequential bound-tightening procedure is proposed to efficiently obtain high-quality solutions. Numerical cases are studied based on two IEHSs of different scales to validate the effectiveness of the proposed co-optimization planning model and the feasibility of the proposed solution methods for solving this complicated planning model for an IEHS.

[en] Highlights: • Virtual energy storage plays a key role in offering flexibility. • Stochastic bid-offer bi-level model of a strategic virtual energy storage merchant. • An all-scenario-feasible stochastic method is first used to the portfolio problem. • The ability of virtual energy storage to mitigate the renewable energy curtailment. • Strategic behaviors result in high profitability withholding production and demand. 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.