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Behnam Mohammadiivatloo - One of the best experts on this subject based on the ideXlab platform.
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risk based scheduling strategy for electric vehicle Aggregator using hybrid stochastic igdt approach
Journal of Cleaner Production, 2020Co-Authors: Parinaz Aliasghari, Behnam Mohammadiivatloo, Mehdi AbapourAbstract:Abstract Electric vehicle Aggregator is an agent for facilitating the interaction between grid and electric vehicle owners, which could bring advantages for all of them. Not only could the Aggregator participate in the day-ahead market as a representative of electric vehicle owners, but the Aggregator could also manage the integrated load of electric vehicles via arranging charging and discharging times in responding to price signals. The current paper presents a novel hybrid stochastic/information gap decision theory optimization technique for decision making of electric vehicle Aggregator in uncertain environment. It evaluates the opportunity/robustness of optimal scheduling of electric vehicle Aggregators facing with uncertainties. The uncertainties of arrival time, departure time and the initial state of charge of each vehicle are modeled via scenarios, while market price uncertainty in the day-ahead market is formulated with a bi-level information gap decision theory based approach focusing on the gap between forecasted and real values. The objective function is to maximize the expected profit of the Aggregator regarding the two contradictory attitudes toward the risk management under the uncertainty of market price, i.e., risk-averse and risk-seeker strategies of information gap decision theory approach. In order to verify the effectiveness of the proposed approach, a case study has been investigated. The results confirm that a risker decision leads to a higher profit. By contrast, the Aggregator can implement robustness function to make a more conservative decision to guaranty his predetermined profit in the face with the uncertainties.
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robust bidding strategy for demand response Aggregators in electricity market based on game theory
Journal of Cleaner Production, 2020Co-Authors: Saeed Abapour, Behnam Mohammadiivatloo, Mehrdad Tarafdar HaghAbstract:Abstract One of the ways to manage and provide flexibility in power systems is demand response (DR). A large number of end-users as DR sources must be aggregated by an intermediate entity called DR Aggregator. This paper proposes an approach based on game theory to obtain the best bidding strategy of DR Aggregators in electricity market. In the presented scheme, an economic responsive load model is employed for DR approach which is based on customer benefit function and price elasticity. In this paper, the network operator receives DR services from the DR Aggregator. It is considered that all bids from Aggregators are assembled by a network operator which calculates the share of each Aggregator in DR programs by revenue function optimization. Furthermore, the network operator offers rewards to DR Aggregators to achieve this purpose. The robust optimization (RO) method is used handling price uncertainty. It is used to optimize the robustness of the decision-making strategies. A non-cooperative game is used to model the competition among DR Aggregators. The Nash equilibrium idea is employed to solve this game.
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risk involved participation of electric vehicle Aggregator in energy markets with robust decision making approach
Journal of Cleaner Production, 2019Co-Authors: Seyyedeh S Barhagh, Behnam Mohammadiivatloo, Amjad Anvarimoghaddam, Somayeh AsadiAbstract:Abstract Uncertainty of different parameters in power systems can put market players at risk. For example, uncertainty of market prices in energy markets with capitalist characteristics can negatively affect decisions of Aggregators participating in such markets to make profits. In fact, uncertainty in capitalist energy markets should be controlled since many individuals who participate in such markets have specific expectations (economic goals in most cases) and this may result in some concerns over the future performances of mentioned markets under uncertainty. It should be noted that Aggregator can be a person or an authority who is responsible for a group of energy consumers/resources like electric vehicles (EVs) and seeks to participate in energy markets in order to get to some expected goals that can be economical, technical and etc. Moreover, attitude to risk of a decision-maker can change in different working conditions which needs to be considered through various decision-making criteria. In this paper, robust performance (optimal/stable operation against the worst possible condition of uncertainty) of an EV Aggregator against the uncertainty of market price is studied by incorporating robust optimization method. The Aggregator willing to participate in energy market can benefit from the provided strategies created by this optimization model to satisfy the expected economic goals and control the uncertainty of energy market. In detail, specific plans and strategies are determined toward possible values of uncertain market price that satisfy economic goals of Aggregator and keep the uncertainty under control. Mixed integer non-linear programming (MINLP) is used to model the problem which is solved in general algebraic modeling system (GAMS) software. A microgrid system composed of distributed generation units (local generation units that can be either renewable or non-renewable) including dispatchable and non-dispatchable ones, EVs and storage system is studied, and the results are presented. The results show that robust optimization model can minimize the daily market cost while mitigating possible risks for the Aggregator of EVs. It should be noted that daily market accounts for energy market in which various energy resources are available to be selected by the related Aggregator in order to supply energy demand.
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self scheduling of demand response Aggregators in short term markets based on information gap decision theory
IEEE Transactions on Smart Grid, 2019Co-Authors: Morteza Vahidghavidel, Nadali Mahmoudi, Behnam MohammadiivatlooAbstract:This paper proposes a new self-scheduling framework for demand response (DR) Aggregators, which contributes over the existing models in the following aspects. The proposed model considers the uncertainties posed from consumers and electricity market prices. Further, the given model applies the information-gap decision theory (IGDT) in the self-scheduling problem, which guarantees the predefined profit by the Aggregator and avoids computational burdens caused by scenario-based methods, such as stochastic programming approaches. The DR Aggregator procures DR from two proposed programs, i.e., reward-based DR and time-of-use. Then, the obtained DR is offered into day-ahead and balancing markets. An IGDT-based profit function is proposed, which leads to a bilevel program. The given bilevel model is then transformed into an equivalent single-level model by developing a non-KKT method, which is solved through commercial solvers available in general algebraic modeling system. The feasibility of the problem is studied using a case study with realistic data of electricity markets.
Jianhui Wang - One of the best experts on this subject based on the ideXlab platform.
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provision of flexible ramping product by battery energy storage in day ahead energy and reserve markets
Iet Generation Transmission & Distribution, 2018Co-Authors: Mushfiqur R Sarker, Jianhui Wang, Fushuan Wen, Weijia LiuAbstract:The variability and uncertainty of renewable energy resources introduce significant challenges to power system operation. One particular example is the occurrence of ramp capability shortage in real-time dispatch, which can cause power balance violations and price spikes. To meet the increasing need for ramp capability, some independent system operators in the USA have led initiatives to promote the implementation of flexible ramping product (FRP). More potential FRP providers, apart from conventional generators, are being explored, among which battery energy storage (BES) appears to be a feasible option owing to its good controllability and fast responsive characteristics. This study proposes an optimisation model for a BES Aggregator to optimally provide FRP in day-ahead energy and reserve markets, aiming to maximise its monetary benefits. The basic concept of FRP is first introduced, including comparisons with traditional ancillary services, pricing mechanisms, and the extensions of market models to integrate FRP. The modes and strategies for BES Aggregators to participate in the electricity markets are then addressed. Case studies indicate that an Aggregator can gain more profit by optimally allocating its resources among various products than only providing energy and reserves. A sensitivity analysis on several key factors is also conducted.
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data driven pricing strategy for demand side resource Aggregators
IEEE Transactions on Smart Grid, 2018Co-Authors: Tianhu Deng, Yonghua Song, Jianhui WangAbstract:We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource Aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource Aggregators. Instead, we assume that the utility can directly observe each Aggregator’s aggregate energy consumption outcomes. Furthermore, the utility can leverage resource Aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of Aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies—a flat-rate scheme and a time-of-use scheme.
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risk based day ahead scheduling of electric vehicle Aggregator using information gap decision theory
IEEE Transactions on Smart Grid, 2017Co-Authors: Jian Zhao, Zhao Xu, Jianhui WangAbstract:In the context of electricity market and smart grid, the uncertainty of electricity prices due to the high complexities involved in market operation would significantly affect the profit and behavior of electric vehicle (EV) Aggregators. An information gap decision theory-based approach is proposed in this paper to manage the revenue risk of the EV Aggregator caused by the information gap between the forecasted and actual electricity prices. The proposed decision-making framework can offer effective strategies to either guarantee the predefined profit for risk-averse decision-makers or pursue the windfall return for risk-seeking decision-makers. Day-ahead charging and discharging scheduling strategies of the EV Aggregators are arranged using the proposed model considering the risks introduced by the electricity price uncertainty. The results of case studies validate the effectiveness of the proposed framework under various price uncertainties.
Mehrdad Tarafdar Hagh - One of the best experts on this subject based on the ideXlab platform.
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robust bidding strategy for demand response Aggregators in electricity market based on game theory
Journal of Cleaner Production, 2020Co-Authors: Saeed Abapour, Behnam Mohammadiivatloo, Mehrdad Tarafdar HaghAbstract:Abstract One of the ways to manage and provide flexibility in power systems is demand response (DR). A large number of end-users as DR sources must be aggregated by an intermediate entity called DR Aggregator. This paper proposes an approach based on game theory to obtain the best bidding strategy of DR Aggregators in electricity market. In the presented scheme, an economic responsive load model is employed for DR approach which is based on customer benefit function and price elasticity. In this paper, the network operator receives DR services from the DR Aggregator. It is considered that all bids from Aggregators are assembled by a network operator which calculates the share of each Aggregator in DR programs by revenue function optimization. Furthermore, the network operator offers rewards to DR Aggregators to achieve this purpose. The robust optimization (RO) method is used handling price uncertainty. It is used to optimize the robustness of the decision-making strategies. A non-cooperative game is used to model the competition among DR Aggregators. The Nash equilibrium idea is employed to solve this game.
Reza Ghorbani - One of the best experts on this subject based on the ideXlab platform.
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non cooperative game theoretic model of demand response Aggregator competition for selling stored energy in storage devices
Applied Energy, 2017Co-Authors: Mahdi Motalleb, Reza GhorbaniAbstract:Abstract Our research is primarily concerned with the construction of a theoretical model of the competition between demand response Aggregators for selling energy previously stored in an aggregation of storage devices (which the Aggregator manages) given sufficient demand from other Aggregators through an incomplete information game. The model culminates in a game-theoretically justifiable decision making procedure for the sellers which may be used to predict and analyze the bids made for energy sale in the market. The methodology for applying the model is worked out in detail for a three-Aggregator case where two players compete with each other for sale to a third. Relevant numerical data for the competition is taken from a real case study which took place on the island of Maui, Hawaii. This market framework is presented as an alternative to the traditional vertically-integrated market structure, which may be better suited for developing demand response and smart grid technologies. We consider two non-cooperative game variants with different market conditions: one competition with no limitation, and one a Stackelberg competition subject to limitations on transaction price and size, each separately with and without inclusion of demand response scheduling (we focus on significant load-bearing thermostatic storage devices such as water heaters, though the principles should be applied generally). Determining the optimal bidding strategies follow the same procedure, and the equilibrium bidding strategies of all others are determined by each player in each case and demonstrates the wide applicability of our methods in each case. Bidding strategy is dependent on parameters inherent to an Aggregator’s energy storage hardware. Demand response scheduling offers greater payoff for Aggregators who implement it, compared with those who do not. Addition of transaction price and power quantity regulations to the market lowers payoffs for all Aggregators participating in the market relative to competition with no limitation.
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a novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture
Applied Energy, 2016Co-Authors: Ehsan Reihani, Mahdi Motalleb, Matsu Thornton, Reza GhorbaniAbstract:With the increasing presence of intermittent renewable energy generation sources, variable control over loads and energy storage devices on the grid become even more important to maintain this balance. Increasing renewable energy penetration depends on both technical and economic factors. Distribution system consumers can contribute to grid stability by controlling residential electrical device power consumed by water heaters and battery storage systems. Coupled with dynamic supply pricing strategies, a comprehensive system for demand response (DR) exist. Proper DR management will allow greater integration of renewable energy sources partially replacing energy demand currently met by the combustion of fossil-fuels. An enticing economic framework providing increased value to consumers compensates them for reduced control of devices placed under a DR Aggregator. Much work has already been done to develop more effective ways to implement DR control systems. Utilizing an integrated approach that combines consumer requirements into aggregate pools, and provides a dynamic response to market and grid conditions, we have developed a mathematical model that can quantify control parameters for optimum demand response and decide which resources to switch and when. In this model, optimization is achieved as a function of cost savings vs. customer comfort using mathematical market analysis. Two market modeling approaches—the Cournot and SFE—are presented and compared. A quadratic function is used for presenting the cost function of each DRA (Demand Response Aggregator) which will be used for settling down the DR market. Contribution of each Aggregator and the final price are presented. Finally, we have also performed sensitivity analysis on the house cost function’s coefficients for one of the Aggregators.
Alireza Ghasempour - One of the best experts on this subject based on the ideXlab platform.
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Finding the optimal number of Aggregators in machine-to-machine advanced metering infrastructure architecture of smart grid based on cost, delay, and energy consumption
2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2016Co-Authors: Alireza Ghasempour, Jacob H. GuntherAbstract:Smart grid uses an advanced metering infrastructure to create a two-way communication network between smart grid components and machine-to-machine communications have a great potential to implement this communication network. In this paper, we propose a one-layer aggregation-based machine-to-machine architecture for advanced metering infrastructure architecture of smart grid and focus on finding the optimum number of Aggregators in its neighborhood area network based on three metrics: energy consumption of each Aggregator, relay and Aggregators cost, and delay. We develop an energy consumption model for each Aggregator. We also define delay and a new performance metric, the product of cost and energy (CE), so that by minimizing it, we can obtain the optimum number of Aggregators. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost, delay, and energy.
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Optimum number of Aggregators based on power consumption, cost, and network lifetime in advanced metering infrastructure architecture for Smart Grid Internet of Things
2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2016Co-Authors: Alireza GhasempourAbstract:Internet of Things connects anythings at any time in any places. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most important components of smart grid which aggregates data from smart meters and sends the collected data to the utility center to be analyzed and stored. In this paper, we have proposed an aggregation-based indirect architecture for advanced metering infrastructure architecture in smart grid Internet of Things and focused on finding the optimum number of regional Aggregators in its neighborhood area network (NAN) based on three metrics: regional Aggregator (RA) power consumption, cost of RAs and wide-area Aggregator, and NAN lifetime. We have developed a power consumption model for each RA and define NAN lifetime based on that. We also defined a new performance metric, the product of power consumption and cost (PC), so that by minimizing it, we can obtain the optimum number of RAs. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between NAN lifetime and RAs' cost.
