G.R. Goyal; S. Vadhera
Abstract
Supply-side energy management (SSEM) aims to improve efficiency in operations and strategic planning. Both the cost of generating electricity and the amount of emissions from that generation are minimized in SSEM. It is required to formulate an optimization problem with these two competing goals in order ...
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Supply-side energy management (SSEM) aims to improve efficiency in operations and strategic planning. Both the cost of generating electricity and the amount of emissions from that generation are minimized in SSEM. It is required to formulate an optimization problem with these two competing goals in order to come up with a compromise. Resolving problems with network reliability caused by peak demand on the electricity system is another goal of SSEM. The ultimate goal of this study is to reduce energy use during peak hours while also cutting down on power losses, generation costs, and pollution from power plants. In this paper all goals of the smart grid system are satisfied and addressed optimally through the use of optimal generator scheduling and an improved demand response technique. To formulate this problem standard IEEE 30-bus system is considered as test boat. The suggested system employs the Cuckoo search method and its most recent variant, adaptive Cuckoo search, to solve a stochastic non-linear optimization problem. The adaptive Cuckoo search approach, when combined with the proposed demand side management strategy, reduces fuel costs by 7.84%, emission dispatch by 16.35%, power losses by 10.31%, and peak hour demand by 15.6%.
V.D. Juyal; S. Kakran
Abstract
Nowadays, the centralized power system is changing to a distributed system, and various energy management systems are being installed for efficient functioning. Load side management is a vital aspect of the energy management of the power network. As residential demand is growing at a high rate, domestic ...
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Nowadays, the centralized power system is changing to a distributed system, and various energy management systems are being installed for efficient functioning. Load side management is a vital aspect of the energy management of the power network. As residential demand is growing at a high rate, domestic customers play a crucial role in the successful implementation of demand response (DR) programs. This paper considers a single customer having a home energy management system (HEMS) for thermostatic and non-thermostatic characteristics-based appliances, photovoltaic panels, an electric vehicle, and a battery energy storage system. The effect of various DR strategies has been discussed. A mixed-integer linear programming-based model of a HEMS is modulated and solved to minimize the electricity consumption cost by employing a real-time price-based DR program using dynamic power import limits. An incentive-based DR program is considered for reducing the energy demand and maintaining the energy balance during peak hours, and peak pricing-based dynamic power import limiting DR programs are included for load shaping. The effect of load shaping on the peak to average ratio is also discussed in different scenarios. Finally, the total electricity price is calculated and analyzed by considering other test cases based on the inclusion/rejection of the mentioned DR programs.
M.A. Baherifard; R. Kazemzadeh; A.S. Yazdankhah; M. Marzband
Abstract
With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important ...
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With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.
J. Salehi; F.S. Gazijahani; A. Safari
Abstract
Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal ...
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Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.
E. Naderi; A. Dejamkhooy; S.J. SeyedShenava; H. Shayeghi
Abstract
Recently due to technical, economical, and environmental reasons, penetration of renewable energy resources has increased in the power systems. On the other hand, the utilization of these resources in remote areas and capable regions as isolated microgrids has several advantages. In this paper, a hybrid ...
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Recently due to technical, economical, and environmental reasons, penetration of renewable energy resources has increased in the power systems. On the other hand, the utilization of these resources in remote areas and capable regions as isolated microgrids has several advantages. In this paper, a hybrid microgrid, which includes photovoltaic (PV)/wind/energy storage, is investigated. It has been located in Iran-Khalkhal. The purposes of this study are optimal energy management and sizing of the microgrid. Since the magnitude of the harvested renewable energy deals severely and complexly with season and climate issues, planning of the system based on their specific values is an oversimplification. Therefore, in addition to conventional constraints such as environmental and operational ones, estimation of the wind speed at the site is considered. The Monte Carlo method is employed to model and estimate wind behavior. Also, for regulating production and demand in the microgrid the Demand Response (DR) program is conducted to improve the contribution of the renewable energy resources. The planning is constructed as an optimization problem. It is formulated as a Mixed Integer Linear Programming (MILP). By solving it, the size and production magnitude of energy sources, as well as storage conditions, are determined. Finally, the proposed method is simulated by GAMS for all seasons of two scenarios. The results show desirable energy management and cost reduction in the studied grid.
A. Afraz; B. Rezaeealam; S.J. SeyedShenava; M. Doostizadeh
Abstract
The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these ...
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The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.
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 ...
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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.
Energy Management
M. Azimi; A. Salami
Abstract
This study presents an optimal framework for the operation of integrated energy systems using demand response programs. The main goal of integrated energy systems is to optimally supply various demands using different energy carriers such as electricity, heating, and cooling. Considering the power market ...
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This study presents an optimal framework for the operation of integrated energy systems using demand response programs. The main goal of integrated energy systems is to optimally supply various demands using different energy carriers such as electricity, heating, and cooling. Considering the power market price, this work investigates the effects of multiple energy storage devices and demand response programs, including the time of use pricing, real-time pricing, and integrated demand response on optimal operation of energy hub. Moreover, impacts of different optimization methods are evaluated on the optimal scheduling of multi-carrier energy systems. Maximizing profits of selling electrical energy and minimizing the purchasing cost of input carrier energies are considered as objective functions to indicate bidirectional interchanges of energy hub systems with the power grid. To minimize the generation cost of energy carriers, a new quadratic objective function is also optimized using genetic algorithm. In this study, optimal operation of the energy hub based on the proposed quadratic objective function is an economic dispatch problem where the purchasing electrical power by the energy hub is considered as a load of the upstream grid. The optimization problem is implemented in the sample energy hub to indicate the effectiveness of different energy storage roles and applied demand response programs in the optimal operation of energy hub systems.
Energy Management
E. Shahryari; H. Shayeghi; B. Mohammadi-ivatloo; M. Moradzadeh
Abstract
Recently, economic and environmental problems have created a strong attitude toward utilizing renewable energy sources (RESs). Nevertheless, uncertainty of wind and solar power leads to a more complicated energy management (EM) of RESs in microgrids. This paper models and solves the EM problem of microgrid ...
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Recently, economic and environmental problems have created a strong attitude toward utilizing renewable energy sources (RESs). Nevertheless, uncertainty of wind and solar power leads to a more complicated energy management (EM) of RESs in microgrids. This paper models and solves the EM problem of microgrid from the generation point of view. To do this, mathematical formulation of a grid- connected microgrid including wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC) and energy storage system (ESS) is presented. Furthermore an improved incentive-based demand response program (DRP) is applied in microgrid EM problem to flatten the load pattern. Comprehensive studying of EM in both intra-day and day-ahead markets is another contribution of this paper. However, the main novelty of this paper is proposing a new uncertainty modeling technique which is based on copula function and scenario generation. This paper tries to optimize operational cost and environmental pollution as the objective functions and solve them using group search optimization (GSO) algorithm. Numerical results approve the efficiency of the proposed method in solving microgrid EM problem.
Micro Grid
A. Marami Dizaji; M. Saniee; K. Zare
Abstract
Resilient operation of microgrid is an important concept in modern power system. Its goal is to anticipate and limit the risks, and provide appropriate and continuous services under changing conditions. There are many factors that cause the operation mode of micogrid changes between island and grid-connected ...
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Resilient operation of microgrid is an important concept in modern power system. Its goal is to anticipate and limit the risks, and provide appropriate and continuous services under changing conditions. There are many factors that cause the operation mode of micogrid changes between island and grid-connected modes. On the other hand, nowadays, electric vehicles (EVs) are desirable energy storage systems (ESSs) because of clean transportation. Besides, energy storage systems are helpful to decrease power generation fluctuations arising from renewable energy sources (RESs) in new power systems. In addition, both sides (EV and RESs’ owners) can gain a good profit by integrating EVs and RESs. Therefore, in this paper, a resilient operation model for microgrid is presented considering disasters and islands from the grid. In the proposed formulation, microgrid (MG) operator schedules its energy resources, EVs and ESSs in minimum cost considering demand response (DR) program and resiliency of the microgrid to islanding and uncertainties in market price, load, and generation of RESs. The impact of uncertainties is modeled in the scenario based framework as stochastic programming. The efficiency of presented method is validated on IEEE standard test system and discussed in two cases.
Micro Grid
V. Amir; Sh. Jadid; M. Ehsan
Abstract
: In this paper, the operation of a future distribution network is discussed under the assumption of a multi-carrier microgrid (MCMG) concept. The new model considers a modern energy management technique in electricity and natural gas networks based on a novel demand side management (DSM) which the energy ...
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: In this paper, the operation of a future distribution network is discussed under the assumption of a multi-carrier microgrid (MCMG) concept. The new model considers a modern energy management technique in electricity and natural gas networks based on a novel demand side management (DSM) which the energy tariff for responsive loads are correlated to the energy input of the network and changes instantly. The economic operation of MCMG is formulated as an optimization problem. In conventional studies, energy consumption is optimized from the perspective of each infrastructure user without considering the interactions. Here, the interaction of energy system infrastructures is considered in the presence of energy storage systems (ESSs), small-scale energy resources (SSERs) and responsive loads. Simulations are performed using MCMG which consists of micro combined heat and power (CHP), photovoltaic (PV) arrays, energy storage systems (ESSs), and electrical and heat loads in grid-connected mode. Results show that the simultaneous operation of various energy carriers leads to a better MCMG performance. Moreover, it has been indicated that energy sales by multi sources to main grids can undoubtedly reduce the total operation cost of future networks.
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 ...
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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 market
M. Khafri; A. Badri; A. A. Birjandi
Abstract
In this paper, a heuristic mathematical model for optimal decision-making of a Distribution Company (DisCo) is proposed that employs demand response (DR) programs in order to participate in a day-ahead market, taking into account elastic and inelastic load models. The proposed model is an extended responsive ...
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In this paper, a heuristic mathematical model for optimal decision-making of a Distribution Company (DisCo) is proposed that employs demand response (DR) programs in order to participate in a day-ahead market, taking into account elastic and inelastic load models. The proposed model is an extended responsive load modeling that is based on price elasticity and customers’ incentives in which they participate in demand response program, voluntarily and would be paid according to their declared load curtailment amounts. It is supposed that DisCo has the ability to trade with the wholesale market and it can also use its own distributed generation (DG), while decision making process. In this regard, at first, DisCo’s optimization frameworks in two cases, with and without elastic load modelings are acquired. Subsequently, utilizing Hessian matrix and mathematical optimality conditions, optimal aggregated load curtailment amounts are obtained and accordingly, individual customer’s load reductions are calculated. Furthermore, effects of DG contributions and wholesale electricity market are investigated. An IEEE 18 bus test system is employed to obtain the results and show the accuracy of the proposed model.
Distribution Systems
Soheil Derafshi Beigvand; Hamdi Abdi,
Volume 3, Issue 2 , December 2015, , Pages 102-115
Abstract
This paper proposes an Optimal Power Flow (OPF) algorithm by Direct Load Control (DLC) programs to optimize the operational cost of smart grids considering various scenarios based on different constraints. The cost function includes active power production cost of available power sources and a novel ...
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This paper proposes an Optimal Power Flow (OPF) algorithm by Direct Load Control (DLC) programs to optimize the operational cost of smart grids considering various scenarios based on different constraints. The cost function includes active power production cost of available power sources and a novel flexible load curtailment cost associated with DLC programs. The load curtailment cost is based on a virtual generator for each load (which participates in DLC program). To implement the load curtailment in the objective function, we consider incentive payments for participants and a load shedding priority list in some events. The proposed OPF methodology is applied to IEEE 14, 30-bus, and 13-node industrial power systems as three examples of the smart grids, respectively. The numerical results of the proposed algorithm are compared with the results obtained by applying MATPOWER to the nominal case by using the DLC programs. It is shown that the suggested approach converges to a better quality solution in an acceptable computation time.
