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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.

Abstract Multi‐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 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.

Abstract This paper addresses the network expansion planning of an active microgrid that utilizes Distributed Energy Resources (DERs). The microgrid uses Combined Cooling, Heating and Power (CCHP) systems with their heating and cooling network. The proposed method uses a bi-level iterative optimization algorithm for optimal expansion and operational planning of the microgrid that consists of different zones, and each zone can transact electricity with the upward utility. The transaction of electricity with the upward utility can be performed based on demand response programs that consist of the time-of-use program and/or direct load control. DERs are CHPs, small wind turbines, photovoltaic systems, electric and cooling storage, gas fired boilers and absorption and compression chillers are used to supply different zones' electrical, heating, and cooling loads. The proposed model minimizes the system's investment, operation, interruption and environmental costs; meanwhile, it maximizes electricity export revenues and the reliability of the system. The proposed method is applied to a real building complex and five different scenarios are considered to evaluate the impact of different energy supply configurations and operational paradigm on the investment and operational costs. The effectiveness of the introduced algorithm has been assessed. The implementation of the proposed algorithm reduces the aggregated investment and operational costs of the test system in about 54.7% with respect to the custom expansion planning method.

There are several reasons why participants, namely conventional participants, and moreover, wind power producers, have been facing an inevitable uncertainty problem in the current short-term electr...

This paper presents a novel approach for optimal electric distribution system expansion planning (OEDSEP) using a hybrid energy hub concept. The proposed method uses an energy hub model to explore the impacts of energy carrier systems on OEDSEP procedure. This algorithm decomposes the OEDSEP problem into three subproblems to achieve an optimal expansion planning of a system in which the investment and operational costs are minimized, while the reliability of the system is maximized. The algorithm was successfully tested in the present research for an urban distribution system.