[Show abstract][Hide abstract] ABSTRACT: This study formulates a security-constrained multi-objective framework with unit commitment in multi-area electricity
markets for day-ahead joint market-clearing. The dynamic and inertial characteristics of the power system are derived and
incorporated into the market-clearing procedure in order to preserve power system security from the frequency viewpoint. In
addition, two novel objective functions (tie-flow deviation index and Rocof index) besides the frequency-dependent social
welfare and frequency excursion index have been defined to control the static frequency, rate of change of frequency (Rocof)
and also the tie-line power flows, following the occurrence of a contingency. Comprehensive analysis tools for multi-area
power systems and also pre- and post-contingency intervals are presented to verify the characteristics of the proposed model.
The developed multi-objective programming is analysed through two case studies, a three-area system scheduled over 1 h and
the IEEE two-area reliability test system over 24 h. It has been shown that the scheduling of energy and reserve services can
be performed more effectively if the system frequency is considered in the market-clearing process. The proposed model can
reconcile the need for reasonable total generation cost with concern for the independent system operator’s responsibilities
about the pre- and post-contingency tie-line power flow control and system security.
IET Generation Transmission & Distribution 06/2014; · 1.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The anticipation of a large penetration of EVs (electric vehicles) into the market brings up many technical issues. The power system may put at risk the security and reliability of operation due to uncontrolled EV charging and discharging. It is necessary to carry out intelligent scheduling for charging and discharging of EVs. In this paper, a smart management and scheduling model is proposed for large number of EVs parked in an urban parking lot. The proposed model considered practical constraints such as desired charging electricity price, remaining battery capacity, remaining charging time and age of the battery. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, EV owners could earn profit from discharging their vehicles as well as having desired SOC (state of charge) in the departure time.
[Show abstract][Hide abstract] ABSTRACT: One of the most important responsibilities of Distribution System Operator (DSO) is to maintain the customer voltage within specified ranges. Capacitor banks have long been used to provide voltage support and to correct displacement power factor on distribution network. This paper presents a new approach for real time voltage control of distribution networks that has improvements over the conventional voltage control models. This approach will be active in emergency conditions where, in real time, the voltages in some nodes leave their permissible ranges. In the proposed model, it is assumed that renewable distributed generations are integrated in the distribution system, and the communication infrastructure of smart grid has already been implemented. Also, all the capacitors are fitted with Remote Terminal Unit (RTU) and are completely accessible and controllable. Unlike previous voltage control methods, the proposed approach does not need the load and renewable generation forecast data to regulate voltage. Moreover, the calculation time of the proposed approach is considerably reduced. The proposed voltage control algorithm is applied on two different models, and each presented model has a substantial improvement over previous models. DSO can choose one of them based on a trade-off between cost and power quality index. To verify the effectiveness and robustness of the proposed control scheme, the developed voltage control scheme is tested on a typical distribution network. The simulation results show that the proposed real time voltage control has the capability to maintain distribution voltage in specified ranges.
[Show abstract][Hide abstract] ABSTRACT: The main goal of Distribution Automation (DA) is the real-time operation, usually without operator intervention, of distribution systems as a consequence of load demand or power generation variations and failure conditions in the distribution systems. As real time voltage control is known as a legacy system that can be fully activated by DA equipments, in this paper an analytical study is reported to demonstrate the effects of load curtailments on voltages profile in distribution network. A new method for real time voltage control, based on emergency demand response program, is also proposed. The proposed method uses the real-time measured data collected by RTUs and determines the tap changer condition and load curtailment required in order to maintain the distribution voltage profile. Emergency conditions include outages of generators and lines, and fluctuations due to unpredictable load demand and renewable generation. A novel voltage sensitivity matrix, based on performed voltage sensitivity analysis due to load participation in demand response program, is also proposed. In order to verify the effectiveness and robustness of the proposed control scheme, it is tested on a typical automated distribution network. Simulation results show that the proper selection of load curtailment can improve voltage profile and that, in emergency conditions, demand response is an effective way to keep the voltage in a permissible range.
[Show abstract][Hide abstract] ABSTRACT: The development of smart grids offers new opportunities to improve the efficiency of operation of Distributed Energy Resources (DERs) by implementing an intelligent Distribution Management System (DMS). The DMS consists of application systems that are used to support the DERs management undertaken by a Distribution System Operator (DSO). In this paper, a conceptual model for a Demand Response Management System (DRMS), conceived as an application system of a DMS, is presented. Moreover, an optimization tool, able to consider the available DERs (conventional or renewable Distributed Generations (DGs) and demand response) is proposed. The optimization tool uses a stochastic multi-objective method in order to schedule DERs and aims at minimizing the total operational costs and emissions while considering the intermittent nature of wind and solar power as well as demand forecast errors. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. The proposed scheduling approach for DERs is tested on a 69-bus distribution test system over a 24-h period.
Electric Power Systems Research 01/2014; 111:156–168. · 1.69 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) into the electric power system may bring up many technical issues. The power system may put at risk the security and reliability of operation due to intermittent nature of renewable generation and uncontrolled charging/discharging procedure of EVs. In this paper, an energy resources management model for a microgrid (MG) is proposed. The proposed method considers practical constraints, renewable power forecasting errors, spinning reserve requirements and EVs owner satisfaction. A case study with a typical MG including 200 EVs is used to illustrate the performance of the proposed method. The results show that the proposed energy resource scheduling method satisfies financial and technical goals of parking lot as well as the security and economic issues of MG. Moreover, EV owners could earn profit by discharging their vehicles’ batteries or providing the reserve capacity and finally have desired State Of Charge (SOC) in the departure time.
Energy Conversion and Management 01/2014; 86:745–755. · 2.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper a stochastic operational scheduling method is proposed to schedule energy and reserve in a smart distribution system with high penetration of wind generation. The wind power and demand forecast errors are considered in this approach and the reserve is furnished by both main grid generators and responsive loads. The consumers participate in both energy and reserve scheduling. A Demand Response Provider (DRP) aggregates loads reduction offers in order to facilitate small and medium loads participation in demand response program. The scheduling approach is tested on an 83-bus distribution test system over a 24-h period. Simulation results show that the proposed stochastic energy and reserve scheduling with demand response exhibits a lower operation cost if compared to the deterministic scheduling.
International Journal of Electrical Power & Energy Systems 01/2014; 63:218–225. · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: When preparing for the widespread adoption of Electric Vehicles (EVs), an important issue is to use a proper EVs’ charging/discharging scheduling model that is able to simultaneously consider economic and environmental goals as well as technical constraints of distribution networks. This paper proposes a multi-objective operational scheduling method for charging/discharging of EVs in a smart distribution system. The proposed multi-objective framework, based on augmented ε-constraint method, aims at minimizing the total operational costs and emissions. The Vehicle to Grid (V2G) capability as well as the actual patterns of drivers are considered in order to generate the Pareto-optimal solutions. The Benders decomposition technique is used in order to solve the proposed optimization model and to convert the large scale mixed integer nonlinear problem into mixed-integer linear programming and nonlinear programming problems. The effectiveness of the proposed resources scheduling approach is tested on a 33-bus distribution test system over a 24-h period. The results show that the proposed EVs’ charging/discharging method can reduce both of operation cost and air pollutant emissions.
Energy Conversion and Management 01/2014; 79:43–53. · 2.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper a stochastic multi-objective economical/environmental operational scheduling method is proposed to schedule energy and reserve in a smart distribution system with high penetration of wind generation. The proposed multi-objective framework, based on augmented ε-constraint method, is used to minimize the total operational costs and emissions and to generate Pareto-optimal solutions for the energy and reserve scheduling problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise non-dominated solution. The wind power and demand forecast errors are considered in this approach and the reserve can be furnished by the main grid as well as distributed generators and responsive loads. The consumers participate in both energy and reserve markets using various demand response programs. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. In order to solve the proposed optimization model, the Benders decomposition technique is used to convert the large scale mixed integer non-linear problem into mixed-integer linear programming and non-linear programming problems. The effectiveness of the proposed scheduling approach is verified on a 41-bus distribution test system over a 24-h period.
Energy Conversion and Management 01/2014; 78:151–164. · 2.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Electric vehicles charging and discharging management as well as large scale intermittent renewable power generation management are known as the two most important challenges in the future distribution system operation and control. Proper integration of these energy sources may introduce a solution for overcoming to challenges. In this paper, a stochastic charging and discharging scheduling method is proposed for large number of electric vehicles parked in an intelligent parking lot where intelligent parking lots are potentially introduced as aggregators allowing electric vehicles interact with the utilities. A self-scheduling model for an intelligent parking lot equipped with photovoltaic system and distributed generators is presented in this paper in which practical constraints, solar radiation uncertainty, spinning reserve requirements and electric vehicles owner satisfaction are considered. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, electric vehicle owners could earn profit by discharging their vehicles as well as having desired state of charge in the departure time.
Journal of the Franklin Institute 01/2014; · 2.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Smart Grids are result of utilizing novel technologies such as distributed energy resources, and communication technologies in power system to compensate some of its defects. Various power resources provide some benefits for operation domain; however, power system operator should use a powerful methodology to manage them. Renewable resources and load add uncertainty to the problem. So, independent system operator should use a stochastic method to manage them. A Stochastic unit commitment is presented in this paper to schedule various power resources such as distributed generation units, conventional thermal generation units, wind and PV farms, and demand response resources. Demand response resources, interruptible loads, distributed generation units, and conventional thermal generation units are used to provide required reserve for compensating stochastic nature of various resources and loads. In the presented model, resources connected to distribution network can participate in wholesale market through aggregators. Moreover, a novel three-program model which can be used by aggregators is presented in this article. Loads and distributed generation can contract with aggregators by these programs. A three-bus test system and the IEEE RTS are used to illustrate usefulness of the presented model. The results show that ISO can manage the system effectively by using this model.
Iranian Journal of Electrical and Electronic Engineering. 01/2014;
[Show abstract][Hide abstract] ABSTRACT: Wind and solar energy introduced significant operational challenges in a Microgrid (MG), especially when renewable generations vary from forecasts. In this paper, forecast errors of wind speed and solar irradiance are modeled by related probability distribution functions and then, by using the Latin hypercube sampling (LHS), the plausible scenarios of renewable generation for day-head energy and reserve scheduling are generated. A two-stage stochastic objective function aiming at minimizing the expected operational cost is implemented. In the proposed method, the reserve requirement for compensating renewable forecast errors is provided by both responsive loads and distributed generation units. All types of customers such as residential, commercial and industrial ones can participate in demand response programs which are considered in either energy or reserve scheduling. In order to validate the proposed methodology, the proposed approach is finally applied to a typical MG and simulation results are carried out.
Energy Conversion and Management 01/2014; 86:1118–1127. · 2.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Renewable DG units are expected to be the key to a sustainable energy supply infrastructure, since they are both inexhaustible and non-polluting. Using renewable distributed generations in distribution system have some advantages such as loss reduction, voltage profile improvement and emission reduction. Therefore, the capacity and location of these units should be determined based on these objectives. In this paper, a placement planning model which determines the capacity, location and type of wind and solar renewable DG has been proposed. The proposed method is based on probabilistic generation-load model. This model considers all possible operating conditions of the renewable DG units with their probabilities, and is used in a deterministic planning problem. Annual energy loss is minimized in this problem, while in all probabilistic operational conditions, problem constraints are observed. The proposed model was tested on a typical distribution system using GAMS software. The results show that a significant reduction in annual energy losses is achieved in the proposed approach.
18th Electric Power Distribution Conference Iran; 04/2013
[Show abstract][Hide abstract] ABSTRACT: Voltage control is one of the imperative issues in the smart distribution control system. While traditional distribution network is equipped with communication and monitoring equipment, the online voltage control can be perfectly achieved. With using these smart grid technologies, the distribution voltage control schemes should carry out intelligently and cover the undesirable effect of high penetration of renewable distributed generation. This paper presents a new approach that improved the conventional voltage control models. The proposed approach needs measuring and communication equipment less than other methods, and can cover the renewable distributed generation impact on distribution network. The proposed online voltage control model was tested on typical distribution network. The results show that the proposed model can stabilize voltage in predefined range in different consumer load fluctuation conditions and variable renewable generation levels.
Journal of Iranian Association of Electrical and Electronics Engineers. 01/2013; 10(2):11-21.
[Show abstract][Hide abstract] ABSTRACT: This study presents a novel stochastic multi-objective model for precise scheduling of energy and reserve services in
day-ahead markets. This model is based on a security-constrained market clearing with an emphasis on preservation of system
frequency. A frequency index that is derived from the total system frequency profile during post-contingency intervals has
been defined as an extra objective function to control system frequency after the occurrence of a contingency. The model is
capable of scheduling the tertiary regulation interval through appropriate generators’ reference power setting instructions made
by the ISO based on participants’ bids for energy and reserve services. The reference power settings are obtained by an
optimisation model and can be performed directly by the speed changers. The load dependency on frequency has also been
analysed in the proposed model. This approach could be used by ISOs to make a trade-off concurrently between system
frequency profile and total operating cost to operate the power system securely in an economically efficient manner. The
developed multi-objective programming is analysed through two case studies; a three-bus system scheduled over 1 h and the
IEEE 24-bus reliability test system over 24 h, solved by means of mixed-integer linear programming methods.
IET Generation, Transmission and Distribution 01/2013;
[Show abstract][Hide abstract] ABSTRACT: This study presents a novel approach for transmission expansion planning (TEP) addressing the inherent uncertainties associated with the estimated investment costs of candidate transmission lines and the forecasted electricity demands during long-term planning horizon. The proposed TEP approach employs a renovated mixed integer linear programming formulation holding the optimality and low computation burden of linear modelling techniques. The above mentioned uncertainties are encountered through robust optimisation methodology enabling the system's planner to assess different levels of uncertainty and conservation throughout planning horizon. The proposed robust TEP procedure is successfully applied to Garver 6-bus, IEEE 30-bus and IEEE 118-bus test systems. Simulation results demonstrate that the uncertainty level of investment costs and electricity demands escalates the total expansion costs based on the scale of power system.
IET Generation Transmission & Distribution 01/2013; 7(11):1318-1331. · 1.41 Impact Factor