• Energy Research

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.

Abstract The Optimal day-ahead Scheduling of Combined Heat and Power (OSCHP) units is a crucial problem in the energy management of Active Distribution Networks (ADNs), especially in the presence of Electrical and Thermal Energy Storages considering Load Commitment (LC) programs. The ADN operator may use Combined Heat and Power (CHP) units to supply its Industrial Customers (ICs) and can transact electricity with the upstream wholesale electricity market. The OSCHP problem is a Mixed Integer Non Linear Programming (MINLP) problem with many variables and constraints. However, the optimal operation of CHP units, Electrical and Thermal Energy Storages considering LC programs 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 the behavior of operation parameters and minimizes the operation costs, verifies the feasibility of the ICs' requested power exchanges and the second stage considers LC programs and the system's stochastic contingency scenarios. The effectiveness of the proposed algorithm has been demonstrated considering 18-bus, 33-bus and 123-bus IEEE test systems.

Abstract From the voltage stability viewpoint, a proper probabilistic analysis of the active distribution networks is essential for distribution system operator to identify and rank the weak buses of system. This paper introduces a probabilistic voltage stability index (VSI) to study the local stability of radial distribution networks including wind generation uncertainty. This index can identify the most sensitive bus to the voltage collapse. The proposed model combines the cumulants with the maximum entropy technique based on a backward/forward sweep technique. The suggested method evaluates not only the voltage magnitude, but also the voltage stability condition of each node in a probabilistic manner. Furthermore, the uncertainties of distributed generation and load demand are taken into account by this index. The proposed methodology is applied to IEEE 33-bus and 69-bus distribution test systems, and the results are interpreted. Four scenarios are studied to assess the impacts of substation voltage changes and different probability density functions of load on the distribution functions of voltage and VSI. Moreover, the effects of voltage dependent load model on the probabilistic VSI are studied. The results of different case studies have demonstrated that the proposed approach is fast and accurate.

Abstract This paper provides a six-level integrated optimization framework for a distribution system that transacts energy with upward electricity market and downward active microgrids in day-ahead and real-time horizons. The proposed method uses a risk-averse formulation and the distribution system utilizes multiple combined heating and power units, distributed generation, plug-in electric vehicles parking lots, and electric and thermal storage units. Demand response program alternatives are also utilized by the distribution system. A three-stage uncertainty modeling is proposed to model six sources of uncertainties that are consist of energy resource power generations, loads and prices, active microgrids contributions and contingencies. Two case studies evaluate the proposed algorithm for the 123-bus test system that multiple 33-bus microgrid systems are transacting energy and ancillary services with the main grid. Further, different sensitivity analyses are performed to evaluate the effect of energy and ancillary services prices on the simulation results.