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Abstract This paper addresses optimal day-ahead scheduling of Combined Heat and Power (CHP) units of an Active Distribution Network (ADN) with Electric Storage Systems (ESS) and Thermal Storage Systems (TSS) considering Industrial Customers (ICs) inter-zonal power exchanges. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upward wholesale electricity market and its downward ICs' systems; meanwhile its ICs can transact energy with each other through the ADN main grid. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear Programing (MINLP) problem with many stochastic and deterministic variables. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs; check the feasibility of the ICs' requested firm and non-firm power exchanges, and the second stage considers the system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjustment of the proposed decision variables in the two stage optimization procedure. The proposed method is applied to 18-bus, 33-bus IEEE test systems. The effectiveness of the proposed algorithm has been investigated.

Abstract Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.

AbstractMulti‐year generation and transmission expansion planning (MY‐G&TEP) is a critical issue in power systems. The present paper considers the optimal placement of Fixed Series Compensation (FSC) and Phase Shifting Transformer (PST) proposes a pool‐market‐based mathematical model (MY‐G&TEP) for maximizing social welfare (SW) and reducing investment costs of transmission lines and new power plants. Following the determination of optimal strategy, the present paper compares the usefulness of PSTs and FSCs in the MY‐G&TEP problem in three scenarios. Since MY‐G&TEP is a complex hybrid, mixed‐integer linear programming (MILP), and nonlinear optimization problem, the YALMIP toolbox and CPLEX solver have been applied to find the optimal solution, a globally optimized solution is obtained. For evaluation, the proposed model has been tested on the IEEE 24‐bus and IEEE 57‐bus systems, and the simulation results indicate that the installation of PST and FSC not only improves market conditions but also increases the flexibility of MY‐G&TEP, and adding these FACTS devices to the studied system leads to an increase in the network's performance and enhancement of objectives of the proposed model.

Abstract This paper presents a two-level optimization problem for optimal day-ahead scheduling of an active distribution system that utilizes renewable energy sources, distributed generation units, electric vehicles, and energy storage units and sells its surplus electricity to the upward electricity market. The active distribution system transacts electricity with multiple downward energy hubs that are equipped with combined cooling, heating, and power facilities. Each energy hub operator optimizes its day-ahead scheduling problem and submits its bid/offer to the upward distribution system operator. Afterwards, the distribution system operator explores the energy hub’s bids/offers and optimizes the scheduling of its system energy resources for the day-ahead market. Further, he/she utilizes a demand response program alternative such as time-of-use and direct load control programs for downward energy hubs. In order to demonstrate the preference of the proposed method, the standard IEEE 33-bus test system is used to model the distribution system, and multiple energy hubs are used to model the energy hubs system. The proposed method increases the energy hubs electricity selling benefit about 185% with respect to the base case value; meanwhile, it reduces the distribution system operational costs about 82.2% with respect to the corresponding base case value.

The simultaneity of power systems development and uncertainty of system elements has promoted the importance of probabilistic load flow (PLF) in the operating and planning studies of the system. This clarifies that the use of the fast and accurate approaches for PLF computation is necessary. To achieve this objective, this study presents an analytical technique, based on the properties of Laplace transform (LT). The suggested methodology is applicable for every continuous probability distribution function as the input random variable. The proposed procedure is applied to the MATPOWER 9‐ and 118‐bus test systems. To validate the combined cumulants and LT (CCLT) technique, the results are compared with the Monte Carlo simulation and the cumulants method combined with the maximum entropy (CCME) principle. The test results show that the proposed approach gives accurate results, with the lower computational burden comparing CCME. Furthermore, the method formulation and case study results demonstrate that the CCLT method is mathematically straightforward and computationally efficient.

Abstract This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system’s energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively.

This article presents a distributed resilient demand response program integrated with electrical energy storage systems for residential consumers to maximize their comfort level. A dynamic real-time pricing method is proposed to determine the hourly electricity prices and schedule the electricity consumption of smart home appliances and energy storage systems commitment. The algorithm is employed in normal and emergency operating conditions, taking into account the comfort level of consumers. In emergency conditions, the power outage of consumers is modeled for different hours and outage patterns. To evaluate the applicability of the proposed model, real samples of Southern California households are considered to model the smart homes and their appliances. Further, a sensitivity analysis is performed to assess the impacts of the number of households and number of persons per household on the output results. The results showed that the proposed model reduced the costs of utility in normal and emergency conditions by about 33.77% and 30.92%, respectively. The values of total payments of consumers in normal and emergency conditions were decreased by about 34.26% and 31.31%, respectively. Further, the consumers comfort level for normal and emergency conditions increased by about 146.78% and 110.2%, respectively. Finally, the social welfare for normal and emergency conditions increased by about 46% and 49.06%, respectively. ; © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; fi=vertaisarvioitu|en=peerReviewed|

Abstract Introducing Combined Heat and Power (CHP) units into Active Distribution Network (ADN) can significantly affect the problem of optimal generation scheduling. A new method for solving the problem of Optimal Scheduling of Combined Heat and Power (OSCHP) units of an ADN with Electric Storage Systems (ESSs) and Thermal Storage Systems (TSSs) considering Industrial Customers (ICs) Inter-Zonal Power Exchanges (IZPEs) is presented. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upstream network. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage stochastic mixed integer linear programming (SMILP) model is utilized to solve the problem to determine the optimal generation scheduling units. The first stage models the behaviour of operation parameters, minimizes the operation costs, and checks the feasibility of the ICs' requested firm and non-firm IZPEs, while the second stage considers system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjusting the proposed decision variables in the two-stage optimization procedure. The proposed method is applied to 18- and 123-bus IEEE test systems to thoroughly demonstrate the benefits of implementing inter-zonal power exchanges.

Summary Market power evaluation and mitigation influence the efficiency of the electricity markets. The traditional indices cannot analyze market power, which caused by capacity withholding of generation companies. This paper describes a new approach to assess capacity withholding. The method is an improvement of a strategy previously proposed by the authors. The contributions of the new approach can be summarized as the following. First, the supply function equilibrium model and Cournot model are used to develop the concept of capacity withholding in electricity markets. Then, distortion–withheld index is calculated according to capacity constraints, reliability, and demand elasticity. Based on distortion–withheld index, the value of capacity withholding index, which shows the cost that independent system operator could spend for market power mitigation, can be obtained. It has been proved that the new approach is able to assess capacity withholding exactly and identify the proper market power mitigation program. The finding in this paper is helpful for market designers and regulators. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract The use of Combined Heat and Power (CHP) with an overall efficiency from 70 to 90% is one of the most effective solutions to optimize the energy consumption. Mainly due to interdependence of the power and heat in these systems, the optimal operation of CHP systems is a complex optimization problem that needs powerful solutions. This paper addresses optimal day-ahead scheduling of CHP units with Electric Storage Systems (ESSs) and Thermal Storage Systems (TSSs) considering security constraints. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear (MINLP) problem with many stochastic and deterministic variables. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs meanwhile the second stage considers the system's stochastic contingency scenarios. The proposed method is applied to 18-bus, 24-bus IEEE test systems. The effectiveness of the proposed algorithm has been investigated.