International Journal of Electrical Power and Energy Systems (01420615)134
This paper presents a new mathematical approach for co-planning of transmission expansion planning and battery storage systems placement. An optimal placement of grid-scaled battery storage systems can help transmission systems in congestion managements and improve power system security. In addition, battery storage systems can increase power system reliability in contingency conditions. In this paper a security and reliability viewpoint is implemented for the simultaneous transmission expansion planning and the optimal placement of battery storage systems. The N-1 security constraints are applied to the expansion planning model. The reliability indices evaluated from second order outages are also involved in the decision making process. The Benders’ decomposition approach is used to reduce the computational burden of the proposed model and make it applicable for large scaled power systems. The IEEE 24-bus test system is used to evaluate the applicability of the proposed method. © 2021 Elsevier Ltd
Ranjbar, H.,
Kazemi, M.,
Amjady, N.,
Zareipour, H.,
Hosseini, S.H. Renewable Energy (09601481)189pp. 618-629
This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance. © 2022 Elsevier Ltd
Applied Sciences (Switzerland) (20763417)11(8)
This paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Ansari m., ,
Zadsar m., ,
Zareipour, H.,
Kazemi, M. International Journal of Electrical Power and Energy Systems (01420615)130
In this paper, a new model is proposed for emergency response of Power Grid Systems (PGS) to severe events in the presence of Natural Gas (NG) storages. The PGS's transmission lines outages as well as NG pipelines outages are integrated in this model. The optimal Network Reserve Requirement (NRR) allocation and NG storage scheduling are studied as emergency response strategies to relief sever events repercussion in PGS. The proposed method is linearized using the combination of piecewise linearization approach and big-M method. The effectiveness of the proposed MILP model to improve system resilience are verified through four case-studies on modified 24-bus IEEE test system integrated to 20-node NG network. © 2021 Elsevier Ltd
Scientia Iranica (23453605)27(3 D)pp. 1413-1423
Utilization of electric vehicle battery to provide frequency regulation service in electricity markets is a technically feasible and economically attractive idea. The role of aggregators as a middleman between electric vehicle owners and the frequency regulation market has been discussed in the literature. However, the economic interaction between the aggregator and the vehicle owners on division of interests is still a missing point. In this paper, a new pricing model for aggregators of electric vehicles is proposed such that not only its profit is maximized, but also the vehicle owners have sufficient incentives to take part in the offered vehicle-to-grid program. In the proposed model, the aggregator takes into account the depreciation cost of electric vehicle batteries and the cost of net energy transaction between the electric vehicles and the grid and, then, considers these items in settling accounts with vehicle owners. The proposed model has been implemented on PJM frequency regulation market, and the results are discussed in the paper. © 2020 Sharif University of Technology. All rights reserved.
Zadsar m., ,
Sebtahmadi, S.,
Kazemi, M.,
Larimi s.m.m., ,
Haghifam m.r., International Journal of Electrical Power and Energy Systems (01420615)118
Resect severe economic losses caused by distribution system equipment outage have highlighted the importance of improving the system resiliency and reliability. In active distribution networks (ADNs), the distributed energy resources (DERs) managed by dynamic isolated microgrids in contingency mode provide an alternative approach to enhance the system resiliency and continue supplying critical loads after equipment outage. How to incorporate this ADNs capability into a short-term DERs scheduling is a challenging issue. In response to this challenge, in this paper, a two stage risk based decision making framework for operation of ADNs is proposed to coordinate 24-h DERs’ scheduling and outage management scheme, in a way to be immune against contingency by creating optimal dynamic multi-micrigrid, micro-turbine and energy storage island operating and load shed plan. The first stage is the normal operation condition that operation cost should be minimized considering of the uncertainties of renewable resources, the electricity price and customers loads. The second stage is the operation in contingency condition. In the second stage, the main objective is to keep the shed load at minimum level. Numerical results and sensitivity analysis from modified 33-bus IEEE network are further discussed to demonstrate the efficiency of the solution approach. © 2019 Elsevier Ltd
Amini, M.H.,
Talari, S.,
Arasteh, H.,
Mahmoudi, N.,
Kazemi, M.,
Abdollahi, A.,
Bhattacharjee, V.,
Shafie-khah, M.,
Siano, P.,
Catalao, J.P.S. Studies in Systems, Decision and Control (21984182)186pp. 167-191
Odetayo, B.,
Kazemi, M.,
Maccormack, J.,
Rosehart, W.D.,
Zareipour, H.,
Seifi, A.R. IEEE Transactions on Power Systems (08858950)33(6)pp. 6883-6893
Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programing approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors, and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short-time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer nonlinear programing optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network. © 2018 IEEE.
IEEE Transactions on Smart Grid (19493053)9(6)pp. 6840-6849
This paper presents a new method for scheduling of battery storage systems for participation in frequency regulation and energy markets, simultaneously. Unknown automatic generation control signal of regulation market is modeled through robust optimization. In addition, the complex effect of participation in regulation market on battery's lifespan is modeled through a dynamic procedure. For this purpose, a long-term optimization process is proposed in which, the short-term participation strategy defines battery's lifespan. In order to prevent fast depreciation of battery due to frequent and deep charges/discharges, a new limiting method is introduced here, which would be useful for participation in regulation market. The proposed long-term model is linearized by implementation of Benders' decomposition. Optimum values for limiting factors are determined in the master problem, while the daily operation strategies are decided by sub-problems. Finally, the applicability of the proposed method is investigated using an illustrative case study. © 2010-2012 IEEE.
Moslemi, N.,
Kazemi, M.,
Abedi, S.M.,
Khatibzadeh-azad, H.,
Jafarian, M. IEEE Systems Journal (19379234)12(4)pp. 3052-3062
This paper presents a new algorithm for systematic coordinated maintenance of the elements of a transmission bay. The proposed method implements a knowledge-based Markov model to introduce a nonperiodic model for maintenance scheduling. The mode-based maintenance scheduling is implemented in this work, which is preferred to element-based one, due to the practical issues. To investigate the effects of maintenance on system's reliability, the network is modeled through its second-order equivalent cut set. Contrary to previous works, which consider each element separately, the presented approach tries to evaluate the optimum maintenance plan for entire elements of a bay by considering all elements of a bay together and coordinating the maintenance plan of each element. In this paper, various types of maintenance plan are considered, and the optimum plan for each element is evaluated using a genetic algorithm. Finally, the practicality of the proposed method is investigated using a typical transmission bay. © 2007-2012 IEEE.
Kazemi, M.,
Zareipour, H.,
Ehsan, M.,
Rosehart, W.D. IEEE Transactions on Power Systems (08858950)32(3)pp. 1949-1959
This paper presents a new approach for determining the day-ahead bidding strategies of a large-scale hybrid electric energy company. The company has both energy generation and energy retailing businesses in a competitive electricity market. Demand response programs are also considered in the retail side of the company in order to hedge the risk of participation in wholesale market. This paper introduces a max-min bilevel mathematical programming with equilibrium constraint model for offering a strategy that manages the risk of uncertain forecasted rivals' bids by robust optimization. The max-min bilevel model is converted to its equivalent single-level optimization using Karush-Kuhn-Tucker optimality conditions. The duality theory is utilized to find the equivalent ordinary maximization model of the max-min problem. Strong duality theory and big M method are also used to linearize the final model of offering strategy. Applicability of the proposed approach is shown by implementing it on the IEEE 118-bus test system. © 1969-2012 IEEE.
Moslemi, N.,
Kazemi, M.,
Abedi, S.M.,
Khatibzadeh-azad, H.,
Jafarian, M. International Transactions on Electrical Energy Systems (20507038)27(4)
Reliability centered maintenance (RCM) is a cost-effective way to maintaining complex systems, transmission network being one. This paper presents a new method for nonperiodic maintenance scheduling of transmission's elements focusing on failure mode analysis. Adopting a nonperiodic mode-based viewpoint to maintenance, this paper introduces 6 plans for maintenance in each failure mode. The related Markov model for each plan is proposed, too. Given these Markov models, attempts are made to determine the best strategy of maintenance and the optimum rate of inspection and maintenance for each mode. Furthermore, to increase the practicality of the presented method and to reduce the overall maintenance cost, we coordinated the maintenance plans of the failure modes of an element, together. Finally, applicability of the presented method is demonstrated using an illustrative example. Copyright © 2016 John Wiley & Sons, Ltd.
Kazemi, M.,
Zareipour, H.,
Amjady, N.,
Rosehart, W.D.,
Ehsan, M. IEEE Transactions on Sustainable Energy (19493029)8(4)pp. 1726-1735
This paper presents a risk-based approach for evaluating the participation strategy of a battery storage system in multiple markets. Simultaneous offering in day-ahead energy, spinning reserve, and regulation markets is considered in this paper. The uncertainties considered include predicted market prices as well as energy deployment in spinning reserve and regulation markets. A new nonprobabilistic model is introduced in this paper to handle the uncertain nature of spinning reserve and regulation markets. Robust optimization is implemented to model these uncertain parameters and manage their related risk. The proposed risk-based model is a max-min problem, which is converted to its equivalent ordinary maximization problem using duality theory. The presented model is linearized by implementing strong duality theory. Finally, the proposed method is tested and verified using an illustrative case study. © 2010-2012 IEEE.
Energy (18736785)106pp. 315-328
In a medium term planning horizon, a retailer should determine its forward contracting and pool participating strategies as well as the selling price to be offered to the customers. Considering a competitive retail electricity market, the number of clients being supplied by any retailer is a function of the selling prices and some other characteristics of all the retailers. This paper presents an equilibrium problem formulation to model the retailer's medium term decision making problem considering the strategy of other retailers. Decision making of any single retailer is formulated as a risk constraint stochastic programming problem. Uncertainty of pool prices and clients' demands is modeled with scenario generation method and CVaR (conditional value at risk) is used as the risk measure. The resulting single retailer planning problem is a quadratic constrained programming problem which is solved using the Lagrangian relaxation method and the Nash equilibrium point of the competitive retailers is achieved by successive solving of this problem for all the retailers. The performance of the proposed method is demonstrated using a realistic case study of Texas electricity market. © 2016 Elsevier Ltd.
International Transactions on Electrical Energy Systems (20507038)26(5)pp. 920-933
This paper presents a risk-constrained programming approach to solve a retailer's medium-term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope-bound information-gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi-level stochastic programming problem based on the information-gap decision theory. The Karush-Kuhn-Tucker optimality conditions are used to convert the bi-level problem into a single-level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. © Copyright 2015 John Wiley & Sons, Ltd.
IEEE Transactions on Power Delivery (19374208)30(2)pp. 684-692
This paper presents an economic study to find the switches that should participate in a switch upgrade plan, based on the results of reconfiguration. The traditional reconfiguration problem trend considers only one loading condition (mostly maximum demand), in order to find the optimum reconfiguration. However, as the distribution loading condition has hourly and daily variations, the optimum configuration continuously changes. Consequently, considering only one loading condition might lead to inefficiency of the results. In this paper, daily load curves of different types of distribution consumers, during various types of days (weekdays and holidays) and seasons (summer and winter), are used to obtain the best reconfiguration hours during a day. Then, genetic algorithm (GA) is used to obtain the optimum configuration during each time interval. The objective function applied to GA consists of loss and energy not supplied. The switches that contribute to reconfiguration should be remotely controlled in order to have the capability of immediate mode alteration. In order to evaluate the feasibility of automated switch installation, the benefit-to-cost ratio is calculated. The entire procedure is applied to a test distribution system, and the results are discussed in this paper. © 1986-2012 IEEE.
IEEE Transactions on Power Systems (08858950)30(1)pp. 376-384
This paper presented a combined scheduling and bidding algorithm for constructing the bidding curve of an electric utility that participated in the day-ahead energy markets. Day-ahead market price uncertainty was modeled using non-probabilistic information gap decision theory (IGDT). The considered utility consisted of generation units and a retailer part; the retailer part of the utility and its demand response program (DRP) could affect the utility's profit, which should be considered in the bidding strategy problem. The bidding strategy algorithm proposed in this paper dispatched units by optimizing the demand response programs of the retailer part. In addition, non-decreasing bidding curve was constructed according to the proposed IGDT-based method. Applicability of the proposed method was demonstrated using an illustrative example with 54 thermal units. Results were verified using after-the-fact actual market data. © 1969-2012 IEEE.
Journal of Natural Gas Science and Engineering (18755100)27pp. 632-640
This paper presents a formulation of fuzzy optimization for uncertainties in natural gas in fuel constraints for hourly individual unit and total unit fuel consumptions. Security Constrained Unit Commitment (SCUC) is usually a mixed integer programming and gas load flow has a non-linear equation a genetic algorithm is proposed to solve natural gas transmission network. A fuzzy mixed integer programming optimization is briefly discussed and adapted to deal with security constrained unit commitment schedule. Finally, two case studies are investigated (IEEE 6-bus system with 7-node natural gas transmission grid and the IEEE 118-bus power system linked with 14-node gas transmission test system) and the SCUC results are discussed and compared for the both cases of fuzzy constraints and crisp model. © 2015 Elsevier B.V.
Electric Power Systems Research (03787796)114pp. 86-92
The present study presents a new risk-constrained bidding strategy formulation of large electric utilities in, presence of demand response programs. The considered electric utility consists of generation facilities, along with a retailer part, which is responsible for supplying associated demands. The total profit of utility comes from participating in day-ahead energy markets and selling energy to corresponding consumers via retailer part. Different uncertainties, such as market price, affect the profit of the utility. Therefore, here, attempts are made to make use of Information Gap Decision Theory (IGDT) to obtain a robust scheduling method against the unfavorable deviations of the market prices. Implementing demand response programs sounds attractive for the consumers through providing some incentives in one hand, and it improves the risk hedging capability of the utility on the other hand. The proposed method is applied to a test system and effect of demand response programs is investigated on the total profit of the utility. © 2014 Elsevier B.V.
A new risk-constrained bidding curve construction method is presented in this paper. A Day-ahead energy market has been chosen for competition of GenCos and the Information Gap Decision Theory (IGDT) is used for modelling the Day-ahead market price uncertainty and its corresponding risk. The bilateral contracts of the GenCo are also considered in the proposed framework. A Bi-level optimization problem is incorporated in the proposed method to guarantee a pre-specified level of revenue. The proposed IGDT based method constructs the non-decreasing bidding curve while dispatching units based on the uncertain forecasted prices of the next-day market. The verification of the proposed method is demonstrated by simulation of a GenCo with 5 thermal units in various day-ahead markets. © 2013 IEEE.
