Evaluation of long-term home electrical appliance efficiency improvement scenarios on load and energy pattern needs comprehensive and precise modeling taking to account a variety of uncertainties. In the modeling process, all effective parameters should be considered. E More
Evaluation of long-term home electrical appliance efficiency improvement scenarios on load and energy pattern needs comprehensive and precise modeling taking to account a variety of uncertainties. In the modeling process, all effective parameters should be considered. Estimation is the main source of information in this process which is a long-term large scale impact assessment procedure. Furthermore, the non homogeneous structure of load behavior in response to DSM policies makes the problem more sophisticated. The presented method implies fuzzy numbers to model the main uncertainties of the demand side reactions to the proposed DSM program. Here, the social classes of customers and their behaviors regarding energy utilization as well as time dependency of the problem parameters are taken into account. The paper focuses on the efficiency improvement of the electric appliances in Iran as a long-term DSM program, due to their considerable share in electricity consumption in residential sector. Finally, the numerical results are presented.
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This paper, presents a new approach for bidding strategy in spot electricity markets. A two-level optimization method is used for profit maximization of non-cooperative firms, while taking into account overall system constrains. In this approach, the market equilibrium More
This paper, presents a new approach for bidding strategy in spot electricity markets. A two-level optimization method is used for profit maximization of non-cooperative firms, while taking into account overall system constrains. In this approach, the market equilibrium points are determined as Nash Equilibria. In order to capture the behavior of all market participants and therefore, a much more competitive environment both the suppliers and consumers are considered as the players of the market. To avoid local maxima solutions, Genetic Algorithm based optimization is incorporated. The proposed method has been applied to IEEE 9 bus system with satisfactory results.
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Demand side management (DSM) is one of the most important methods which have been used to maximize the benefits of the electric power market participants. In the deregulated power systems, DSM is called demand response (DR). In this paper, two DR programs have been focu More
Demand side management (DSM) is one of the most important methods which have been used to maximize the benefits of the electric power market participants. In the deregulated power systems, DSM is called demand response (DR). In this paper, two DR programs have been focused: time-of-use (TOU) and emergency demand response program (EDRP). In this paper DR is modeled considering both TOU and EDRP methods, simultaneously, using the single and multi period load models, based on the load elasticity concept. The proposed model is implemented on the peak load of the Iranian Power Grid and the optimum prices for TOU program and the optimum incentives for combined TOU and EDRP programs are determined.
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This paper presents a new multiobjective model for optimal placement of distributed generation sources in electric distribution network under load and market price uncertainties that finds out the non-dominated multiobjective solutions corresponding to the simultaneous More
This paper presents a new multiobjective model for optimal placement of distributed generation sources in electric distribution network under load and market price uncertainties that finds out the non-dominated multiobjective solutions corresponding to the simultaneous minimization of economic cost, technical risks, and economical risk due to uncertainties. Fuzzy sets theory is used to model the uncertainties. The proposed model is solved using a specialized genetic algorithm as the optimization tool. The performance of the proposed approach is assessed and appreciated by case study on a typical distribution network.
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In this paper a new concept has been proposed to consider “Energy Efficiency” as a new commodity. The new commodity is based on DSM (Demand Side Management) and especially the strategic conservation techniques in the restructured environment. In the proposed method the More
In this paper a new concept has been proposed to consider “Energy Efficiency” as a new commodity. The new commodity is based on DSM (Demand Side Management) and especially the strategic conservation techniques in the restructured environment. In the proposed method the ability to improve efficiency is traded as a new commodity by some new players.
The effect of new commodity trading is analyzed on the long-term market power reduction in parallel to available long-term solutions that mostly rely on generation solution.
The numerical study according to real world data shows the potential and effectiveness of the proposed method.
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, wind farms are used to generate electric power in some parts of the world. With increasing penetration level of wind farms in electric power systems, modification of current tools to evaluate and manage the system is an important issue. Evaluation of total transfer ca More
, wind farms are used to generate electric power in some parts of the world. With increasing penetration level of wind farms in electric power systems, modification of current tools to evaluate and manage the system is an important issue. Evaluation of total transfer capability (TTC) is one of the considerable tools in restructured power systems which is used to schedule future transactions between areas in multi area power systems to ensure security of network. In this paper, a method is proposed for probabilistic evaluation of TTC of multi area power systems in the presence of wind farms. Firstly, a general approach based on Monte Carlo simulation is used to simulate a system state considering system load and power output of wind farm and optimal power flow (OPF) is used to calculate TTC level for each state. Then risk analysis is used as a decision making tool to determine the appropriate TTC level for a fixed system load level. Finally, both of system load and power output of wind farm are considered and clustered input data are used to accelerate Monte Carlo convergence speed. To demonstrate the effectiveness of the proposed approaches IEEE-RTS is used.
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This paper proposes a comprehensive framework for distributed energy resource (DER) expansion planning from investors’ viewpoint based on a combination of dynamic programming algorithm and game theory. In this framework, different aspects of DER planning i.e. their unce More
This paper proposes a comprehensive framework for distributed energy resource (DER) expansion planning from investors’ viewpoint based on a combination of dynamic programming algorithm and game theory. In this framework, different aspects of DER planning i.e. their uncertainties, risks, pollution, etc. are included. Wind turbines, gas engines and demand response (DR) programs are considered as DERs in this study. The intermittent nature and uncertainty of wind power generation and also uncertainty of demand response programs will cause the investors to consider risk in their investment decisions. In order to overcome this problem, a modified model has been derived to study the regulatory intervention impacts on wind expansion planning and implementing DR programs. Dynamic programming method is utilized for this problem solving and in each step, the Nash equilibrium point is calculated using Cournot model. A model based on intermittent nature of wind power generation and uncertainties of DR programs is developed which can calculate the optimal investment strategies. The effectiveness of the proposed model is proved through implementing on a test system.
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Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for u More
Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for optimal operation of the EH.
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