Abstract:We are very pleased to present to you the special section of the Journal of Modern Power Systems and Clean Energy on the coordinated planning, operation, and control of electricity and natural gas infrastructures. This special section aims at addressing the existing challenges in integrated planning, operation and control of natural gas and electric power systems that will enhance the resilience, economics, efficiency, reliability sustainability, and security of both infrastructures. We had invited original submissions from various countries focusing on the computational and technological aspects of the integrated natural gas and electric power systems. The development of sustainable, affordable, and clean sources of energy is considered a prerequisite for today’s global economic strength and will benefit tomorrow’s resilient society. Under the impetus of competition in the energy industry, the unbundling of the electricity sector has introduced new technologies for the generation and the delivery of electricity, which signify less pollutant, highly efficient, and less costly ways of supplying the electricity. The large-scale integration of variable and renewable generation units will require an installed generating capacity that can respond quickly in power systems, and the deployment of natural gas-fired generation units will continue to serve this critical mission and play an indispensable role in power system operation and control. It is envisioned that cheap supply of natural gas, abundant quantity of renewable energy, and greater use of smart grid for promoting customer participations in managing the daily electricity load will reshape the global energy profile in upcoming years. However, there are many challenges that require further research and development corresponding to the planning, operation and control of integrated natural gas and electricity infrastructures. New tools are desired to manage the coupling between natural gas and electric power system, and innovative methods are sought to analyze the impact of uncertainty on the integrated energy systems. This special section includes a total of eleven articles which discuss the above issues, introduce analytical methods for solving the pertinent problems, and present the results in detail in order to introduce the audience with the integration of these two very critical and highly diverse energy infrastructures. The eleven articles included in the special section are summarized and categorized into four groups as follows: 1) Planning of integrated natural gas and power systems; 2) Economic operation of integrated natural gas and power systems; 3) Network analyses of integrated natural gas and power systems; 4) Market issues for integrated natural gas and power systems.

Abstract:The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources. This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.

Abstract:As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way to plan natural gas and power systems. In this paper, the co-planning process is formulated as a mixed integer nonlinear programming problem to address emerging challenges, such as system reliability evaluation, market time line mismatch, market uncertainties, demand response effect, etc. An innovative expansion co-planning (ECP) framework is established in this paper to find the best augmentation plan which comes with the minimum cost. Specifically, to cope with uncertainties in market share, decision analysis is introduced. Meanwhile, the energy conversion efficiency between gas and electricity in the coupled load center is considered in the ECP constraints. Comprehensive case studies are applied to validate the performance of proposed approach.

Abstract:One of the main factors impacting the reliability of energy systems nowadays is the growing interdependence between electricity and gas networks due to the increase in the installation of gas-fired units. Securityconstrained unit commitment (SCUC) models are used to economically schedule generating units without compromising the system reliability. This paper proposes a novel SCUC formulation that includes dynamic gas constraints, such as the line pack, and transmission contingencies in power and gas networks for studying the integrated system reliability. A Benders’ decomposition with linear programming techniques is developed to be able to study large systems. By including dynamic gas constraints into the SCUC, the proposed model accounts for the flexibility and reliability that power systems require from gas systems in the short term. Case studies of different size and complexity are employed to illustrate how the reliability of one system is affected by the reliability of the other. These experiments show how both systems operate in a secure way (by including contingencies) increases operating costs by approximately 9% and also show how these costs can vary by 24% depending on the line pack scheduling.

Abstract:The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen (P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub (EH) with both a P2H facility (electrolyzer) and a gas-to-power (G2P) facility (hydrogen gas turbine) is proposed to accommodate a high penetration of wind power. The EH is modeled and integrated into a security-constrained unit commitment (SCUC) problem, and this optimization problem is solved by a mixed-integer linear programming (MILP) method with the Benders decomposition technique. Case studies are presented to validate the proposed model and elaborate on the technological potential of integrating P2H into a power system with a high level of wind penetration (HWP).

Abstract:This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman’s rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Le′vy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.

Abstract:In this paper a combined optimization of a coupled electricity and gas system is presented. For the electricity network a unit commitment problem with optimization of energy and reserves under a power pool, considering all system operational and unit technical constraints is solved. The gas network subproblem is a medium-scale mixed-integer nonconvex and nonlinear programming problem. The coupling constraints between the two networks are nonlinear as well. The resulting mixed-integer nonlinear program is linearized with the extended incremental method and an outer approximation technique. The resulting model is evaluated using the Greek power and gas system comprising fourteen gas-fired units under four different approximation accuracy levels. The results indicate the efficiency of the proposed mixedinteger linear program model and the interplay between computational requirements and accuracy.

Abstract:The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics. This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub, with collocated gas-fired units, power-to-gas (PtG) facilities, and natural gas storages, is considered to store or convert one type of energy (i.e., electricity or natural gas) into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation (C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

Abstract:This paper aims to optimize total energy costs in an operational model of a novel energy hub (EH) in a residential area. The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic (PV) generation, solar heat exchanger (SHE), and a battery energy storage system (BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price, capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational effi- ciency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.

Abstract: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 multislack-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.

Abstract:t The increasing adoption of gas-fired power plants directly strengthens the coupling between electric power and natural gas systems. Current industrial practice in optimal power flow for electric power systems has not taken the security constraints of gas systems into consideration, resulting in an overly-optimistic solution. Meanwhile, the operation of electric power and natural gas systems is coupled over multiple periods because of the ramp rate limits of power generators and the slow dynamical characteristics of gas systems. Based on these motivations, we propose a multi-period integrated natural gas and electric power system probabilistic optimal power flow (M-GEPOPF) model, which includes dynamic gas flow models. To address the uncertainties originating from wind power and load forecasting, a probabilistic optimal power flow (POPF) calculation based on a three-point estimate method (3PEM) is adopted. Moreover, power-togas (PtG) units are employed to avoid wind power curtailment and enable flexible bi-directional energy flows between the coupled energy systems. An integrated IEEE RTS 24-bus electric power system and the Belgian 20-node natural gas system are employed as a test case to verify the applicability of the proposed M-GEPOPF model, and to demonstrate the potential economic benefits of PtG units.

Abstract:This paper explores the market structures of natural gas and electricity as well as the interdependence of natural gas prices and bids with increasing reliance on natural gas as the penetration of renewable energy resources increases in order to complement their intermittencies. In particular, the paper will attempt to answer the following two questions: What could the generation mix look like in 2030 with a renewable-rich generation landscape and how could this impact gas prices? How do gas- fired generator (GFG) generation volatility, their prices, and their bids for gas change between 2015 and 2030 with increased penetration of renewables? In order to answer these questions, computational models are derived using forecasting and regression analysis tools and an auction model.

Abstract:Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas (PtG) technology has attracted much research attention in recent years. PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.

Abstract:The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.

Abstract:The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

Abstract:From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

Abstract:The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, PairCopula theory has been introduced to construct a multivariate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed. Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.