Prediction-Correction Affine Energy Flow Algorithm for Integrated Electricity and Natural Gas System

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

Guest Editorial: Situational awareness of integrated energy systems

The joint operation of natural gas system and power system is an important direction of energy system integration. In this paper, the unit combination modeling method of integrated gas-electric energy system is studied, and the operation constraints such as output, climbing and duration considering unit startup and shutdown state are established. The matrix balance model of power system and natural gas system is established. Based on the generation transfer distribution factor, the tie line security constraints are proposed, and the simplified method of gas electric coupling model of gas turbine based on the piecewise linearization method is proposed. Finally, 6-node power system and 6-node natural gas system are used to build an integrated gas electric energy system. Taking the economic cost optimization as the optimization goal, it is verified that the unit combination constraint in this paper can effectively optimize the unit operation dispatching in the combined system.

In order to improve energy efficiency and solve environmental pollution problems, integrated electricity and natural gas (IENG) system has become the first choice. It is of great significance to study the coupling mode, planning and operation of IENG. In this paper, according to the uncertainties of load and renewable energy, a robust optimization method is adopted. IENG system considering power to gas (PtG) and gas turbine (GT) is modeled. On this basis, an optimal planning model is proposed, and the C&CG algorithm is used to solve the problem. A case study illustrates the influence of different conditions on the optimal planning results, and the operation results are also analyzed.

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.

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-and-constraint 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.

Co-operation of electricity and natural gas systems including electric vehicles and variable renewable energy sources based on a continuous-time model approach

While proposing dual carbon strategy goals, research on carbon reduction in integrated energy systems (IESs) has become a focus of attention. Traditional IES carbon emission flow is mostly based on energy flow, without considering the differences in energy quality. Exergy can be used as a reasonable measure of energy quality. Combining the theory of exergy flow and carbon emission flow, a modeling and analysis method for IES carbon emission flow considering energy quality is proposed. First, exergy flow models for electric and natural gas systems, equivalent exergy flow models for heat systems, and exergy hub models are introduced. Afterward, the exergy branch losses are allocated to the nodes at both ends. A networked modeling method for the carbon emission flow of energy stations is proposed. A unified matrix calculation model for IES carbon emission flow considering energy quality is established. On this basis, an IES carbon emission flow calculation method considering energy quality is proposed. Finally, this method is applied to the test IES, and the results show that the carbon emission flow calculation results reflect the distribution of carbon emissions borne by the effective energy supplied by the source and consumed by the load in the system, taking into account both the quantity and quality of energy. The reasonable allocation of new energy is conducive to synchronously improving the energy efficiency, exergy efficiency, and cleanliness of the system.

This paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges.

Corrigendum: Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs with P2G Storage and Wind PowerName of all authors as they appear in the published original article Affiliations of all authors as they appear in the published original version of the article * Correspondence: Tao Jiang, tjiang@neepu.edu.cnKeywords: Power-to-gas, ramping cost, wind power accommodation, stochastic programming, natural gas systemCorrigendum on: Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs With Power-to-Gas Storage and Wind Power. Front. Energy Res. 8:596774. doi: 10.3389/fenrg.2020.596774. Incorrect Funder NameThere is an error in the Funding statement. The correct Name for the Funder is ** science and technology project of State Grid Jilin Corporation (SGJL0000DKJS2000287)**. This is the only funder. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

The proliferation of gas turbines and power to gas technologies have increasingly intensified the interaction between the power system and natural gas system. This paper proposes a distributed method to evaluate the total energy supply capability (TESC) of integrated power distribution and natural gas system (IDGS). Firstly, a mathematical TESC evaluation model is developed for IDGS, and the difference of convex procedure (DCP) is utilized to deal with the nonconvex AC power flow and Weymouth gas flow equations. Then, the alternating direction multiplier method (ADMM) algorithm is adopted to decompose the model and solve it in a distributed manner, which can preserve the privacy of the distribution system and natural gas system. Numerical studies validate the effectiveness of the proposed model and solution methodology.

The energy flow calculation of the integrated electric power and natural gas system (IEGS) is generally tackled by the classical Newton–Raphson (NR) method with second‐order convergence. However, this method may fail to converge or incur oscillating iterations leading to heavy computation burden if the initial point is not selected properly, especially in heterogenous integrated energy systems containing the natural gas system. To handle this problem, a variant of Newton–Raphson method with third‐order convergence is proposed, which needs one function and two derivative evaluations per iteration without increasing the number of derivative evaluations. In order to verify the effectiveness of the proposed method, simulation studies are carried out on several different cases for the energy flow calculation of IEGS. Experiment results reveal that the proposed method is superior to the classical Newton–Raphson method and its other variants in terms of computational efficiency.

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.

To use more green electricity, more renewable energy sources are employed in the distribution power systems. The combination of wind power with the hydrogen electrolyzer unit is one of effective ways to utilize renewable energy by converting electricity to hydrogen. In addition, more natural gas-fired units increase the couplings between the power systems and the natural gas systems. Therefore, it is necessary to conduct coordinated scheduling for integrated power and natural gas systems in consideration of hydrogen electrolyzer units that are used to produce hydrogen from wind power. The revenue from hydrogen production is established as a function of a part of wind power. To consider the distribution topology, the spanning tree constraints are used to ensure the radiality of the distribution system. The DistFlow model and the piecewise linear method are used to deal with the nonlinear models of the distribution system and the natural gas system. Furthermore, the carbon emission is included in the optimization objective to guarantee an optimal carbon emission with an optimal operational point. The entire problem is established as mixed integer linear programming model. Tests on a revised IEEE 33-bus system associated with a 20-node natural gas system show that the coordinated scheduling on integrated power and natural gas systems with hydrogen production from wind power can achieve an optimal operational cost.

This paper aims to solve the optimal power and gas flow problem of the integrated electricity and natural gas networks. Gas-fired power plants provide linkage between electricity and natural gas networks. The model of natural gas network consisting of gas sources, loads, pipelines and compressors is calculated by the Newton-Raphson method. Afterwards, a multi-objective group search optimizer with adaptive covariance and Levy flights (MGSO-ACL) is used to solve the operation problem of the optimal power and gas flow of the integrated electricity and natural gas networks. The fuel cost and pollutant emission of the integrated electricity and natural gas system are considered as objective functions. Furthermore, a decision making method based on the evidential reasoning (ER) is utilized to determine a final optimal solution from the Pareto-optimal frontier obtained by the MGSO-ACL. The application of the proposed methods is tested on an 8-node natural gas network integrated with the modified IEEE-30 test system through a gas-fired generator.