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The variability in the generation dispatch of the natural gas generation units will lead to fluctuation in natural gas demand profile that could further jeopardize the security of the natural gas network. The coordinated operation of electricity and natural gas infrastructure systems would help to improve the security and reliability measures in both infrastructure systems and mitigate the risk of demand curtailment. The electricity and natural gas network operation problems are non-convex mixed-integer nonlinear programming problems that are hard to solve in polynomial time. The non-convex feasible regions are formed by the Weymouth constraint and the introduced binary commitment decision variables in the natural gas and electricity network operation problems, respectively. This paper utilized a sparse semidefinite programming (SDP) relaxation to procure the optimal solution for the coordinated operation of electricity and natural gas networks. The presented algorithm leverages the sparseness of the natural gas network to construct several small matrices of lifting variables that are used to form a tight and traceable SDP relaxation. A set of valid constraints that tighten the relaxation ensures the exactness of the solution procured from the relaxed problem. The effectiveness of the presented approach is shown in case studies. [ABSTRACT FROM AUTHOR]

This paper presents the expansion planning for data centers and data routes in the data and electricity networks considering the uncertainties in the planning horizon to ensure an acceptable rate of service to the requests received from the end-users in the data network. The objective is to determine the location and capacity of the data centers as well as the required data routes while considering the imposed constraints in the electricity and data networks. The installation cost of data centers and data routes, as well as the expected operation cost of the data centers, are minimized. The proposed problem addressed the uncertainties in the expansion planning of the electricity networks including the availability of renewable generation resources, the variations in electricity demand, the availability of generation and transmission components in the electricity network, and the uncertainties in the number of requests received by the user groups in the data network. The problem is formulated as a mixed integer linear programming problem and Bender decomposition and electricity price signals are used to capture the interaction among the data and electricity networks. The presented case study shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Wireless charging station (WCS) enables in-motion charging of the electric vehicles (EVs). This paper presents the short-term operation of WCS by capturing the interdependence among the electricity and transportation networks. In the transportation network, the total travel cost consists of the cost associated with the travel time and the cost of utilized electricity along each path. Each EV takes the path that minimizes its total travel cost. In the electricity network, the changes in WCS demand as a result of changes in the traffic flow pattern impacts the price of electricity. The changes in the price of electricity further affect the charging strategy of the EVs and the associated traffic flow pattern. The coordination between electricity and transportation networks would help mitigate congestion in the electricity network by routing the traffic flow in the transportation network. The presented formulation leverages decentralized optimization to address the economic dispatch in the electricity network as well as the traffic assignment in the transportation network. The presented case studies highlight the merit of the presented model and the developed algorithms for the coordinated operation of WCS in electricity and transportation networks. [ABSTRACT FROM AUTHOR]

This paper presents an approach to transform the active distribution network with distributed energy resources into multiple autonomous microgrids. The distribution network consists of several generation resources and demand entities, that are clustered into autonomous microgrids. The proposed problem is formulated as a bi-level optimization problem that leverages the Eigen decomposition in the graph spectra of the distribution network to determine the boundaries for microgrids and a mixed-integer programming problem that minimizes the expansion cost within microgrids. The presented approach is evaluated in a case study for a distribution network considering the imposed reliability constraints. The outcomes indicate the effectiveness of the proposed algorithm to determine the expansion strategies to form autonomous microgrids in active distribution networks. [ABSTRACT FROM PUBLISHER]

This paper proposes an approach to formulate the multiple-period ramping capability of dispatchable generation resources and evaluates the impact of this service on the generation scheduling in day-ahead electricity market. It is discussed that the multiple-period ramping enhances the load following capability of dispatchable generation resources and improves the dispatchability of renewable energy resources in power systems. The presented approach encompasses the uncertainties in the operation scheduling of power systems, using scenario based stochastic security-constrained unit commitment. The presented case studies also highlight the merits of integrating energy storage facilities to reduce the ramping services provided by dispatchable generation resources with respective costs. [ABSTRACT FROM PUBLISHER]

This paper proposed a hierarchical structure for the electricity market to facilitate the coordination of energy markets in distribution and transmission networks. The proposed market structure enables the integration of microgrids, which provide energy and ancillary services in distribution networks. In the proposed hierarchical structure, microgrids participate in the energy market at the distribution networks settled by the distribution network operator (DNO), and load aggregators (LAs) interact with microgrids and generation companies (GENCOs) to import/export energy to/from the distribution network electricity markets from/to the wholesale electricity market. The proposed approach addressed the synergy of energy markets by introducing dynamic game with complete information for GENCOs, microgrids, and LAs. The proposed hierarchical competition is composed of bi-level optimization problems in which the respective upper-level problems maximize the individual market participants’ payoff, and the lower-level problems represent the market settlement accomplished by the DNO or the independent system operator. The bi-level problems are solved by developing sensitivity functions for market participants’ payoff with respect to their bidding strategies. A case study is employed to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

With the increase in the investment in the gas-fired electricity generation technology, capturing the operation constraints of the natural gas network in the electricity and natural gas operation problems becomes more crucial. The non-convexity in the feasibility region formed by natural gas network constraints will impede achieving the global solution for these problems. This letter proposed an algorithm to procure a tight and tractable convex relaxation for the natural gas network constraints that can be leveraged in the electricity and/or natural gas network operation problems. The merit of the proposed algorithm is illustrated using a case study and the efficiency of the proposed formulation is compared with mixed integer linear and semidefinite programming formulations. [ABSTRACT FROM AUTHOR]

This paper proposed a methodology to identify the vulnerable components, and ensure the resilient operation of coordinated electricity and natural gas infrastructures considering multiple disruptions within the microgrid. The microgrid demands, which consist of electricity and heat demands, are served by the interdependent electricity and natural gas supplies. The proposed approach addressed the vulnerability of multiple energy carrier microgrids against various interdictions, which is used to apply preventive reinforcements to increase the resilience of energy supply and decrease the operation cost. The proposed methodology is formulated as a bi-level optimization problem to address the optimal and secure operation of multiple energy carrier microgrids. The interdependence between natural gas and electricity infrastructures is addressed to show the effectiveness of the presented methodology in improving the resilience of generation and demand scheduling against deliberate actions causing disruptions in the interdependent energy infrastructures in multiple energy carrier microgrids. [ABSTRACT FROM PUBLISHER]

Thanks to the contribution of our reviewers, in 2021, the median time to first decision was 17 days and the median time to publication was 40 days. The editors would like to extend their gratitude and recognition to the following reviewers for their precious time and dedication, regardless of whether the papers they reviewed were finally published: Rigorous peer-reviews are the basis of high-quality academic publishing. [Extracted from the article]

This work aims to minimize the cost of installing renewable energy resources (photovoltaic systems) as well as energy storage systems (batteries), in addition to the cost of operation over a period of 20 years, which will include the cost of operating the power grid and the charging and discharging of the batteries. To this end, we propose a long-term planning optimization and expansion framework for a smart distribution network. A second order cone programming (SOCP) algorithm is utilized in this work to model the power flow equations. The minimization is computed in accordance to the years (y), seasons (s), days of the week (d), time of the day (t), and different scenarios based on the usage of energy and its production (c). An IEEE 33-bus balanced distribution test bench is utilized to evaluate the performance, effectiveness, and reliability of the proposed optimization and forecasting model. The numerical studies are conducted on two of the highest performing batteries in the current market, i.e., Lithium-ion (Li-ion) and redox flow batteries (RFBs). In addition, the pros and cons of distributed Li-ion batteries are compared with centralized RFBs. The results are presented to showcase the economic profits of utilizing these battery technologies. [ABSTRACT FROM AUTHOR]