Power System Operation
N. Kumar; S. Dahiya; K.P.Singh Parmar
Abstract
The microgrid (μG) is an integration of distributed generation and local loads with energy storage system. Cost minimization is one of the main objectives in modern power systems.Economic dispatch(ED) is a fundamental problem related to μG and the conventional grid. Economic dispatch(ED) provides ...
Read More
The microgrid (μG) is an integration of distributed generation and local loads with energy storage system. Cost minimization is one of the main objectives in modern power systems.Economic dispatch(ED) is a fundamental problem related to μG and the conventional grid. Economic dispatch(ED) provides the optimal output of generators in order to reduce the total operating cost. Emission dispatch (EMD) is one of the other major problems associated with CG. The emission dispatch (EMD) solution provides the optimal generator operation to reduce harmful pollutants for a specific load demand. Multi-objective economic emission dispatch (MEED) provides a compromise between ED and EMD. In this paper, two test systems have been proposed. Test system one consists of Six CG. Static ED, EMD, and MOEED analysis has been provided for test system one. Test system two consists of four CG, One wind turbine generator (WTG), and one photovoltaic module (PVM).This paper intends to provide sensitivity analysis and uncertainty regarding the curtailment cost of RES. CPLEX solver in GAMS has been proposed to optimize the three fundamental problems. Comparative study and sensitivity analysis show optimal results, and the GAMS solver provides a more comprehensive framework. Reduction in cost due to uncertainty in ED is 9.58% as compared to 9.7% for test system two. The cost has been reduced in MEED by 9.33% as compared to 9.46%. MEED comparison shows the increment in cost of 2.66 %, but the emission is reduced by 18.98 % for test system two.
Power System Operation
S. Ghaderi; H. Shayeghi; Y. Hashemi
Abstract
In this paper, a model for hybrid transmission expansion planning (TEP) and reactive power planning (RPP) considering demand response (DR) model has been presented. In this study RPP considered by TEP for its effects on lines capacity and reduction of system expansion costs. On the other hand the expansion ...
Read More
In this paper, a model for hybrid transmission expansion planning (TEP) and reactive power planning (RPP) considering demand response (DR) model has been presented. In this study RPP considered by TEP for its effects on lines capacity and reduction of system expansion costs. On the other hand the expansion of the transmission system is an important subject, especially dealing with the new issues of smart networks like as demand response. Demand response program can change the network expansion planning by shifting elasticity loads and reducing of peak load to improve conditions and decrease the costs. To combine demand response model into the transmission expansion planning and reactive power planning, nonlinear mixed integer meta-heuristic optimization algorithm is used. To evaluate the impact of the proposed expansion planning, this model is exerted to the 30-bus test system. Simulation outcomes display the proposed technique considering demand response model reduces the overall cost of the hybrid TEP-RPP.
Power System Operation
A. Rastgou; S. Bahramara
Abstract
Unit commitment (UC) problem tries to schedule output power of generation units to meet the system demand for the next several hours at minimum cost. UC adds a time dimension to the economic dispatch problem with the additional choice of turning generators to be on or off. In this paper, in order ...
Read More
Unit commitment (UC) problem tries to schedule output power of generation units to meet the system demand for the next several hours at minimum cost. UC adds a time dimension to the economic dispatch problem with the additional choice of turning generators to be on or off. In this paper, in order to improve both the exploitation and exploration abilities of the firefly algorithm (FA), a new modification approach based on the mutation and crossover operators as well as an adaptive formulation is applied as an adaptive modified firefly algorithm (AMFA). In this paper, it is shown that AMFA can solve the UC problem in a better manner compared to the other meta-heuristic methods. The method is applied on some case studies, a typical 10-unit test system, 12, 17, 26, and 38 generating unit systems, and IEEE 118-bus test system, all with a 24-hour scheduling horizon. Comparison of the obtained results with the other methods addressed in the literature shows the effectiveness and fastness of the applied method.
Power System Operation
H. Siahkali
Abstract
The operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different ...
Read More
The operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new fuzzy based modeling approach is implemented to develop the new formulation of power system problems under an uncertain environment with energy storage systems. Interval type-2 fuzzy membership function (MF) is implemented to model the uncertainty of available wind power generation and the type-1 fuzzy MF is used to model the other parameters in weekly unit commitment (UC) problem. The proposed approach is applied to two different test systems which have conventional generating units, wind farms and pumped storage plants to consider differences between the type-1 and type-2 fuzzy approaches for uncertainty modeling. The results show that the total profit of UC problem using type-2 fuzzy MF is better than type-1 fuzzy MF.
Power System Operation
M. R. Behnamfar; H. Barati; M. Karami
Abstract
This study addresses a stochastic structure for generation companies (GenCoʼs) that participate in hydro-thermal self-scheduling with a wind power plant on short-term scheduling for simultaneous reserve energy and energy market. In stochastic scheduling of HTSS with a wind power plant, in addition to ...
Read More
This study addresses a stochastic structure for generation companies (GenCoʼs) that participate in hydro-thermal self-scheduling with a wind power plant on short-term scheduling for simultaneous reserve energy and energy market. In stochastic scheduling of HTSS with a wind power plant, in addition to various types of uncertainties such as energy price, spinning /non-spinning reserve prices, uncertainties of RESs, such as output power of the wind power plant are also taken into account. In the proposed framework, mixed-integer non-linear programming of the HTSS problem is converted into a MIP. Since the objective of the study is to show how GenCosʼ aim to achieve maximum profit, mixed-integer programming is used here. Therefore, to formulate the MIP for the problem of HTSS with a wind power plant in the real-time modeling, some parameters like the impact of valve loading cost (VLC) that are accompanied by linear modeling, are considered. Furthermore, in thermal units, parameters such as prohibited operating zones (POZs) and different uncertainties like the energy price and wind power are included to formulate the problem more suitably. The point that is worth noting is the use of dynamic ramp rate (DRR). Also, the application of multi-functional curves (L) of hydro plants is considered when studying inter-unit scheduling. Finally, the required tests are conducted on a modified IEEE 118-bus system to verify the accuracy and methodology of the proposed method.
Power System Operation
A. Niromandfam; A. Sadeghi Yazdankhah; R. Kazemzadeh
Abstract
The regulatory schemes currently used for reliability improvement have weaknesses in the provision of quality services based on the customers’ perspective. These schemes consider the average of the service as a criterion to incentivize or penalize the distribution system operators (DSOs). On the ...
Read More
The regulatory schemes currently used for reliability improvement have weaknesses in the provision of quality services based on the customers’ perspective. These schemes consider the average of the service as a criterion to incentivize or penalize the distribution system operators (DSOs). On the other hand, most DSOs do not differentiate electricity services at the customer level, due to the status of the electricity grid and lack of adequate information about customers’ preferences. This paper proposes a novel reliability insurance scheme (RIS), which enables the electricity consumers to determine their desired reliability levels according to their preferences and pay corresponding premiums to the DSO. The DSO can use the premiums to improve reliability or reimburse consumers. To design efficient insurance contracts, this paper uses utility function to estimate customers’ viewpoints of electricity energy consumption. This function measures the customers’ satisfaction of electricity energy consumption. The proposed utility based reliability insurance scheme (URIS) may create a free-riding opportunity for the DSO, in which low quality service is provided and the collected premiums are used to pay the reimbursements. To prevent free-riding opportunity, this paper incorporates the proposed URIS and reward/penalty schemes (RPSs). The results show that the success of the proposed reliability scheme increases as the grid flexibility increases.
Power System Operation
H. Mousavi-Sarabi; M. Jadidbonab; B. Mohammadi ivatloo
Abstract
The impact of different energy storages on power systems has become more important due to the development of energy storage technologies. This paper optimizes the stochastic scheduling of a wind-based multiple energy system (MES) and evaluates the operation of the proposed system in combination with ...
Read More
The impact of different energy storages on power systems has become more important due to the development of energy storage technologies. This paper optimizes the stochastic scheduling of a wind-based multiple energy system (MES) and evaluates the operation of the proposed system in combination with electrical and thermal demand-response programs and the three-mode CAES (TM-CAES) unit. The proposed wind-integrated MES consists of a TM-CAES unit, electrical boiler unit, and thermal storage system which can exchange thermal energy with the local thermal network and exchange electricity with the local grid. The electrical and thermal demands as well as wind farm generation are modeled as a scenario-based stochastic problem using the Monte Carlo simulation method. Afterwards, the computational burden is reduced by applying a proper scenario-reduction algorithm to initial scenarios. Finally, the proposed methodology is implemented to a case study to evaluate the effectiveness and appropriateness of the proposed method.
Power System Operation
R. Kazemzadeh; M. Moazen
Abstract
Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale ...
Read More
Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time.
Power System Operation
H. M. Samakoosh; M. Jafari-Nokandi; A. Sheikholeslami
Abstract
Virtual power plant (VPP) is an effective approach to aggregate distributed generation resources under a central control. This paper introduces a mixed-integer linear programming model for optimal scheduling of the internal resources of a large scale VPP in order to maximize its profit. The proposed ...
Read More
Virtual power plant (VPP) is an effective approach to aggregate distributed generation resources under a central control. This paper introduces a mixed-integer linear programming model for optimal scheduling of the internal resources of a large scale VPP in order to maximize its profit. The proposed model studies the effect of a demand response (DR) program on the scheduling of the VPP. The profit of the VPP is calculated considering different components including the income from the sale of electricity to the network and the incentives received by the renewable resources, fuel cost, the expense of the purchase of electricity from the network and the load curtailment cost during the scheduling horizon. The proposed model is implemented in a large scale VPP that consists of five plants in two cases: with and without the presence of the DR. Simulation results show that the implementation of the DR program reduces the operation cost in the VPP, therefore increasing its profit.
Power System Operation
S. Halilčević; I. Softić
Abstract
This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects ...
Read More
This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects of generation. In the past, the optimal scheduling of electricity generation resources has been based on the optimal activation levels of power plants over time to meet demand for the lowest cost over several time periods. At the same time, the result of that type of optimization is single-dimensional and constrained by numerous limitations. To avoid an apparently optimal solution, a new concept of optimality is presented in this paper. This concept and the associated algorithm enable one to calculate the measure of a system’s state with respect to its optimal state. The optimal system state here means that the fuzzy membership functions of the considered attributes (the characteristics of the system) have the value of one. That particular measure is called the “degree of optimality” (DOsystem). The DOsystem can be based on any of the system's attributes (economy, security, environment, etc.) that take into consideration the current and/or future state of the system. The calculation platform for the chosen electric power test system is based on one of the unit commitment solvers (in this paper, it is the genetic algorithm) and fuzzy logic as a cohesion tool of the outcomes obtained by means of the unit commitment solver. The DO-based algorithm offers the best solutions in which the attributes should not to distort each other, as is the case in a strictly deterministic nature of the Pareto optimal solution.
Power System Operation
A. Rabiee
Abstract
- Power system state estimation is a central component in energy management systems of power system. The goal of state estimation is to determine the system status and power flow of transmission lines. This paper presents an advanced state estimation algorithm based on weighted least square (WLS) ...
Read More
- Power system state estimation is a central component in energy management systems of power system. The goal of state estimation is to determine the system status and power flow of transmission lines. This paper presents an advanced state estimation algorithm based on weighted least square (WLS) criteria by introducing virtual meters. For each bus of network, except slack bus, a virtual meter is considered, using the concept of KCL law. Regarding virtual meter, an improved state estimation algorithm is obtained with higher accuracy and lower computation burden. In the case study, at first, a simple 6-bus test system is presented and the proposed state estimation algorithm is followed step by step. Then, in order to evaluate the effectiveness and applicability of algorithm, IEEE 30-bus and IEEE 118-bus test systems are also taken into consideration. The obtained results verify the usefulness of the proposed method in large size power systems including thousands of buses.
Power System Operation
E. Heydarian-Forushani; H. Aalami
Volume 4, Issue 2 , December 2016, , Pages 104-116
Abstract
Increasing the penetration of variable wind generation in power systems has created some new challenges in the power system operation. In such a situation, the inclusion of flexible resources which have the potential of facilitating wind power integration is necessary. Demand response (DR) programs and ...
Read More
Increasing the penetration of variable wind generation in power systems has created some new challenges in the power system operation. In such a situation, the inclusion of flexible resources which have the potential of facilitating wind power integration is necessary. Demand response (DR) programs and emerging utility-scale energy storages (ESs) are known as two powerful flexible tools that can improve large-scale integration of intermittent wind power from technical and economic aspects. Under this perspective, this paper proposes a multi objective stochastic framework that schedules conventional generation units, bulk ESs, and DR resources simultaneously with the application to wind integration. The proposed formulation is a sophisticated problem which coordinates supply-side and demand-side resources in energy and up/down spinning reserve markets so that the cost, emission, and multi objective functions are minimized separately. In order to determine the most efficient DR program which can potentially coordinate with bulk ESs in the system with a significant amount of wind power, a comprehensive DR programs portfolio including time- and incentive-based programs is designed. Afterwards, strategy success index (SSI) is employed to prioritize DR programs from independent system operator (ISO) perspective. The IEEE-RTS is used to reveal the effectiveness of the proposed method.
Power System Operation
Ehsan Dehnavi; Hamdi Abdi,; Farid Mohammadi
Volume 4, Issue 1 , June 2016, , Pages 29-41
Abstract
Nowadays, demand response programs (DRPs) play an important role in price reduction and reliability improvement. In this paper, an optimal integrated model for the emergency demand response program (EDRP) and dynamic economic emission dispatch (DEED) problem has been developed. Customer’s behavior ...
Read More
Nowadays, demand response programs (DRPs) play an important role in price reduction and reliability improvement. In this paper, an optimal integrated model for the emergency demand response program (EDRP) and dynamic economic emission dispatch (DEED) problem has been developed. Customer’s behavior is modeled based on the price elasticity matrix (PEM) by which the level of DRP is determined for a given type of customer. Valve-point loading effect, prohibited operating zones (POZs), and the other non-linear constraints make the DEED problem into a non-convex and non-smooth multi-objective optimization problem. In the proposed model, the fuel cost and emission are minimized and the optimal incentive is determined simultaneously. The imperialist competitive algorithm (ICA) has solved the combined problem. The proposed model is applied on a ten units test system and results indicate the practical benefits of the proposed model. Finally, depending on different policies, DRPs are prioritized by using strategy success indices.
Power System Operation
E. Babaei; N. Ghorbani
Volume 3, Issue 1 , June 2015, , Pages 23-33
Abstract
Reliability investigation has always been one of the most important issues in power systems planning. The outages rate in power system reflects the fact that more attentions should be paid on reliability indices to supply consumers with uninterrupted power. Using reliability indices in economic dispatch ...
Read More
Reliability investigation has always been one of the most important issues in power systems planning. The outages rate in power system reflects the fact that more attentions should be paid on reliability indices to supply consumers with uninterrupted power. Using reliability indices in economic dispatch problem may lead to the system load demand with high reliability and low probability of power's outage rate. In this paper, the Economic Dispatch (ED) problem is optimized using the reliability indices. That is, ED problem and system reliability are proposed as Combined Economic Dispatch and Reliability (CEDR) problem. In CEDR problem, it is tried to utilize generating units in a way that we have high reliability in supplying the system load demand as well as the minimum fuel costs. Due to multi-objective and non-convex characteristics of this problem, Particle Swarm Optimization with Smart Inertia Factor (PSO-SIF) is used to solve the problem. In this research, the ED of power plants is successfully implemented in two systems with 6 and 26 generating units considering emission and system reliability.
Power System Operation
A. Badri; K. Hoseinpour Lonbar
Volume 3, Issue 1 , June 2015, , Pages 34-46
Abstract
This paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (EVs). The operational framework is a two-stage programming model in which at the first stage, the retailer ...
Read More
This paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (EVs). The operational framework is a two-stage programming model in which at the first stage, the retailer and EV aggregator do their medium-term planning. Determination of retailer's optimum selling price and the amount of energy that should be purchased from bilateral contracts are medium-term decisions that are made one month prior to real-time market. At the second stage, market agents deal with their activities in the short-term period. In this stage the retailer may modify its preliminary strategy by means of pool market option, interruptible loads (ILs), self-scheduling and EVs charging and discharging (V2G). Thus, a bi-level programming is introduced in which the upper sub-problem maximizes retailer profit, whereas the lower sub-problem minimizes the aggregated EVs charging and discharging costs. Final decision making is obtained in this stage that may be considered as a day-ahead market, keeping in mind the medium-term decisions. Due to the volatility of pool price and uncertainties associated with the consumers and EVs demand, the proposed framework is a mixed integer nonlinear stochastic optimization problem; therefore, Monte Carlo Simulation (MCS) is applied to solve it. Furthermore, a market quota curve is utilized to model the uncertainty of the rivals and obtaining retailer's actual market share. Finally, a case study is presented in order to show the capability and accuracy of the proposed framework.
Power System Operation
R. Sedaghati; F. Namdari
Volume 3, Issue 1 , June 2015, , Pages 47-55
Abstract
One of the significant strategies of the power systems is Economic Dispatch (ED) problem, which is defined as the optimal generation of power units to produce energy at the lowest cost by fulfilling the demand within several limits. The undeniable impacts of ramp rate limits, valve loading, prohibited ...
Read More
One of the significant strategies of the power systems is Economic Dispatch (ED) problem, which is defined as the optimal generation of power units to produce energy at the lowest cost by fulfilling the demand within several limits. The undeniable impacts of ramp rate limits, valve loading, prohibited operating zone, spinning reserve and multi-fuel option on the economic dispatch of practical power systems are scrutinized in this paper. Thus, the proposed nonlinear non-convex formulation is solved by a new modified version of bio-inspired bat algorithm. Due to the complexities associated with the large-scale optimization problem of economic dispatch, adaptive modifications are added to the original bat algorithm. The modification methods are applied at two separate stages and pledge augmentation in convergence rate of the algorithm as well as extricating the algorithm from local optima. Veracity of the proposed methodology are corroborated by performing simulations on three IEEE test systems.
Power System Operation
S.M. Mohseni-Bonab; A. Rabiee; S. Jalilzadeh; B. Mohammadi-Ivatloo; S. Nojavan
Volume 3, Issue 1 , June 2015, , Pages 83-93
Abstract
Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of ...
Read More
Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained multi objective (MO) optimization problem considering two objectives, i.e., minimization of active power losses and voltage deviations from the corresponding desired values, subject to full AC load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformers’ tap ratios and shunt reactive power compensation at the weak buses. The proposed probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed probabilistic MO-ORPD problem.
