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With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.

Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices play a crucial role. The more accurate the prediction is, the lower the market risk is. In this paper, several machine learning algorithms (Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including Mean Absolute Error (MAE), R-index, Mean Absolute Percentage Error (MAPE), and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and Tree-based models outperform other models at different time horizons.

In this paper, estimation of optimal capacity and location of installation of wind, solar and fuel cell sources in distribution systems to reduce loss and improve voltage profile, by considering load changes, is carried out with Lightning Search Algorithm (LSA). Studies have been conducted on the standard IEEE 33 bus power system in two scenarios. In the first scenario, study is done with the assumption that the load remains constant throughout the project period and in the second scenario with the load growth. The results of the simulation, indicated that the puissance of load changes on distributed generations (DGs) placement studies. Also, the results confirm the performance of the proposed algorithm in reducing the objective function., 6 pages, 14 figures, 3 tables. To appear in the XIV 2020 IEEE Texas Power and Energy Conference (TPEC), 6-7 February 2020, Texas, U.S.A

In this study, the problem of fault zone detection of distance relaying in FACTS-based transmission lines is analyzed. Existence of FACTS devices on the transmission line, when they are included in the fault zone, from the distance relay point of view, causes different problems in determining the exact location of the fault by changing the impedance seen by the relay. The extent of these changes depends on the parameters that have been set in FACTS devices. To solve the problem associated with these compensators, two instruments for separation and analysis of three-line currents, from the relay point of view at fault instance, have been utilized. The wavelet transform was used to separate the high-frequency components of the three-line currents, and the support vector machine (using methods for multi-class usage) was used for classification of fault location into three protection regions of distance relay. Besides, to investigate the effects of TCSC location on fault zone detection of distance relay, two places, one in fifty percent of line length and the other in seventy-five percent of line length, have been considered as two scenarios for confirmation of the proposed method. Simulations indicate that this method is effective in the protection of FACTS-based transmission lines., 6 pages, 1 figure, 11 tables, To appear in the XIV, 2020 IEEE Texas Power and Energy Conference (TPEC), 6-7 February, 2020, Texas, U.S.A

Price forecasting is a crucial element for the members of the electricity markets and business decision making to maximize their profits. The electricity prices have an impact on the behavior of market participants, and thus, predicting prices for generation companies, and consumers is essential for both the short-term profits in the Day-Ahead, Intra-Day and Ancillary markets, and the long-term benefits in the future planning, investment, and risk management. Therefore, participants in the electricity market need to accurately and effectively predict the price signal to manage market risk. In this paper, different forecasting models have been compared, and the most promising ones have been employed to forecast the short term Italian electricity market clearing price for achieving forecasting accuracy. In particular, simulations are performed for four principal regression methods, including Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron. The performance of predicted models is compared through several performance metrics, including MAE, RMSE, R, and the total number of percentage error anomalies. The results indicate the SVM is the best choice for forecasting the electricity market price on the Italian case study.

During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers. In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions. Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs on an 11-bus MG over a 24-h period and also a 14-bus MG over a period of 336 h (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required.

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

In recent years, demand response programs have attracted attentions for reducing network peak. Demand Response (DR) is the variation of energy consumption by customers in response to incentives, aiming to reduce consumption at peak hours. In general, DR methods might be based on price or incentive; this paper studies Interruptible/Curtailable (I/C) Demand Response Program. In this paper, the effect of changes in incentives and penalties on the implementation of I/C service on the microgrid is studied considering different uncertainties. The microgrid studied in this paper can exchange power with the upstream network. Effect of implementing I/C service in different modes on operation cost, customer profit, load factor, and peak reduction is evaluated in this paper. To prove the effectiveness of the presented method, this model is implemented on an 11-bus microgrid in a 24-hour period, and the results are investigated.

– Due to the increasing need of electrical energy and environmental issues corresponding to fossil fuels such as global warming, renewable energy sources attracted a lot of engineers and researchers to themselves. Among renewable energy sources, wind energy as the most accessible energy source, has been among the most popular research topics in recent years. Doubly-Fed Induction Generator (DFIG) is a commonly used configuration for variable speed Wind Energy Conversion Systems (WECSs) because of its independent control of active and reactive powers and extensive range of wind speed variations. In this paper, a Pulse Width Modulation (PWM) rectifier and a Z-Source Inverter (ZSI) are used as Grid Side Converter (GSC) and Rotor Side Converter (RSC), respectively. Z-Source Inverter is a newly proposed power electronic converter which has solved the limitations and disadvantages of conventional voltage source and current source inverters. A modified Space Vector Modulation (SVM) technic based on Sliding Mode generator power control is used to control the switching pattern of the ZSI. To verify the effectiveness of the proposed control system, simulations result in MATLAB/Simulink software are presented. Different wind speeds as well as independent control of active and reactive powers are examined through these simulations.

Penetration of wind units in Microgrid (MG) imposes remarkable challenges on MG operation. Demand Response Programs (DRPs) and Energy Storage Units are used by MG operators to address these challenges. This paper analyzes the effect of running the Time-of-Use Demand Response Program (TOU-DRP) on an isolated MG by considering different capacities of installed wind power with/without energy storage unit. The energy storage unit is deployed to cover the stochastic nature of wind generation unit. TOU-DRP is modeled based on price elasticity and customer benefit function in an isolated MG. Different levels of customers’ participation in TOU-DRP has also been studied and its effects on operation cost, unserved energy, and wind power spillage are investigated comprehensively. To verify the proposed model’s efficiency, the study is implemented on an 11-bus MG over a 24-h period for twelve detailed case studies. The case study results confirmed the effectiveness of the proposed model in running DRP and providing MG operator a general overview for optimal operation.

Today, with the movement of power systems towards competition and breaking the monopoly, the importance of ancillary services like reactive power and voltage control has increased. One of the most important services is power reactive service. Independent System Operator (ISO)has to provide reactive power in deregulated environments which are considered one of the six ancillary services of power system which should be provided by the ISO to improve system security. ISO is responsible to provide reactive power in deregulated environments. Purpose of this paper is to present a method for pricing reactive power. In this method, reactive power pricing is performed based on Financial Transmission Rights (FTRs). The novelty of this study is that it has offered a new method for pricing reactive power based on FTR. Reactive power pricing methods presented so far have not considered congestion costs of the transmission system but this method considers congestions costs also through considering FTR. In this paper, results of this method are evaluated on a standard IEEE-30 bus test system and compared with current pricing method used for pricing reactive power in Iran.

Due to their complicated dynamics coupled with kinematics and non-holonomic underactuated behavior, spherical robots cannot unveil their full maneuverability when linear controllers are applied. Because of that, position and trajectory tracking of spherical robots have been the main challenges recently tackled in the literature. In this paper, two controllers are introduced to provide the effective path following of a 2-DOF spherical robot. A sliding mode controller (SMC) and a fuzzy sliding mode controller (FSMC) are designed and compared. The Lyapunov stability theorem is employed to analyze the stability of the controller and the tracking error. The simulation results indicate that the spherical robot controlled by the proposed methods is capable of moving to a desired point from any given initial point with minimum tracking error.

Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit. In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer’s scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers’ risk and increases its profit. From the customers’ perspective also the retailers’accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers’ load shifting on the reduction of retailer’s risk, as well as the reduction of retailer’s payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8%, under demand response activation. Consequently, it provides a win–win solution for both retailer and its customers.

Optimal microgrid operation is one of the most important challenges for microgrid operators. Decreasing the operation cost while the problem constraints are fulfilled is the aim of operators in this challenge. The demand response program in presence of uncertainties in different components of the microgrid is also attracted a lot of attention in recent years. In this paper, the impact of running Direct Load Control (DLC) program and different positions of installing storage units on microgrid are investigated. The microgrid is connected to the up-grid and DLC demand response program (DLC-DRP) is modeled based on price elasticity and customers benefit as well as finding the proper location of installing storage units (batteries) by considering various factors, is the objective of this paper. GAMS optimization software and Mixed Integer Non-Linear Programming (MINLP) technic have been deployed to achieve this goal. To verify the performance of the proposed method, the results of running DLC-DRP on an 11-bus microgrid in 24 hours period has been analyzed.

In this paper, simultaneous employment of electrical vehicle-to-grid (V2G) and wind power generation in security constrained unit commitment (SCUC) problem are considered. SCUC problem as one of the most highlighted research area in electrical power system provides a commitment scheduling table for the generation units in which generation operator aims to maximize system security as well as to minimize generation costs along with the satisfying system and units constraints. The technology of V2G as a new energy resource absorbed remarkable consideration, recently. V2G reduces the dependence of generation procedure to the small and costly thermal units and subsequently has tremendously impact on diminishing operation costs as well as ameliorate of load vacillations management. Besides the V2G, utilization of renewable energies like wind-based generation gained considerable attention in last decade. In this paper, simultaneous employment of V2G and wind power in scheduling and operation of power systems considering the uncertainty of wind generation is presented. Numerical results of independent use of V2G and simultaneous utilization of V2G and wind-based generation are provided and effects of such the obligations on the reduction of generation costs and enhancement of operation indexes and penetration of wind farms in power systems are investigated.