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The growing interdependencies between natural gas and power systems, driven by gas-fired generators and gas compressors supplied by electricity, necessitates detailed investigation of the interactions between these vectors, particularly in the context of growing penetration of renewable energy sources. In this research, an expansion planning model for integrated natural gas and power systems is proposed. The model investigates optimal investment in flexibility options such as battery storage, demand-side response, and gas-fired generators. The value of these flexibility options is quantified for gas and electricity systems in Great Britain in 2030. The results indicate that the flexibility options could play an important role in meeting the emission targets in the future. However, the investment costs of these options highly impact the future generation mix as well as the type of reinforcements in the natural gas system infrastructure. Through the deployment of the flexibility options up to £24.2 billion annual cost savings in planning and operation of natural gas and power systems could be achieved, compared to the case that no flexibility option is considered.

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a b s t r a c tIn this paper, a new approach is presented for developing optimal double-sided bidding strategy in security-constrained electricity markets by considering emission of pollutants, as further objectives. In the proposed methodology, both Generation Companies (GenCos) and Distribution Companies (DisCos) try to maximize their profit by implementation of optimal strategies, whiles they have incomplete information about their rivals and market mechanism of payment is locational marginal pricing. In addition, each participant provides its strategic bids based on supply function equilibrium model and it modifies its bidding strategies until Nash equilibrium points are computed. The proposed approach is modeled as a bi-level optimization problem with the upper sub problem addressing individual GenCos and DisCos and the lower sub problem addressing the Independent System Operator (ISO). The upper level maximizes the individual market participant's profit and the lower one solves the ISO's market clearing problem for maximizing Community Welfare Function (CWF). A Self-adaptive Global-based Harmony Search Algorithm (SGHSA) is used to obtain optimal bidding strategies. The proposed methodology has been implemented on the 6-machine 8-bus system test system to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results illustrate the profitableness of the newly developed approach in the obtaining optimal bidding strategies.

Summary This paper points out the application of artificial neural network for short‐term load forecasting where the projected loads are utilized to define a discrete‐time state transition model (i.e., process model). The model is applied to estimate states dynamically and to generate pseudo measurements. Weights of neural network are not treated static and would be carried out under reevaluation alongside the estimation of state vector dynamically. The unscented Kalman filter estimation approach, which requires less approximation of power system, is used in the proposed method. The unscented Kalman filter is implemented through a dual structure due to the interactions of the state vector and the dynamic model of power system. The performance of the proposed method from accuracy prospective is compared with a couple of widely used methods. An optimum solution for wide‐area monitoring system would be realized through implementation of more realistic process model along the simplicity of the proposed method and its capability to handle hybrid measurement data from SCADA and phasor measurement units.

As a result of fossil fuel prices and the associated environmental issues, electric vehicles (EVs) have become a substitute for fossil-fueled vehicles. Their use is expected to grow significantly in a short period of time. However, the widespread use of EVs and their large-scale integration into the power system will pose numerous operational and technical challenges. To avoid these issues, it is essential to manage the charging and discharging of EVs. EVs may also be considered sources of dispersed energy storage and used to increase the network’s operation and efficiency with reasonable charge and discharge management. This paper aims to provide a comprehensive and updated review of control structures of EVs in charging stations, objectives of EV management in power systems, and optimization methodologies for charge and discharge management of EVs in energy systems. The goals that can be accomplished with efficient charge and discharge management of EVs are divided into three groups in this paper (network activity, economic, and environmental goals) and analyzed in detail. Additionally, the biggest obstacles that EVs face when participating in vehicle-to-grid (V2G) applications are examined in this paper.