Showing 12 results for Demand
L. Ghods, M. Kalantar,
Volume 6, Issue 3 (9-2010)
Abstract
Prediction of peak loads in Iran up to year 2011 is discussed using the Radial
Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the
current and future trends is carried out for global grid of Iran. Predictions were done for
target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load
forecasting is mainly affected by economy factors rather than weather conditions. This
study focuses on economical data that seem to have influence on long-term electric load
demand. The data used are: actual yearly, incremental growth rate from previous year, and
blend (actual and incremental growth rate from previous years). As the results, the
maximum demands for 2007 through 2011 are predicted and is shown to be elevated from
37138 MW to 45749 MW for Iran Global Grid. The annual average rate of load growth
seen per five years until 2011 is about 5.35%
L. Ghods, M. Kalantar,
Volume 7, Issue 4 (12-2011)
Abstract
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
S. Salarkheili, A. Akbari Foroud, R. Keypour,
Volume 7, Issue 4 (12-2011)
Abstract
In this paper capacity withholding in an oligopolistic electricity market that all Generation Companies (GenCos) bid in a Cournot model is analyzed and the capacity withheld index, the capacity distortion index and the price distortion index are obtained and formulated. Then a new index, Distortion-Withheld Index (DWI), is proposed in order to measure the potential ability of market for capacity withholding. In these indices the impact of demand elasticity on capacity withholding is considered and it is shown that demand elasticity plays an important role for capacity withholding and market power mitigation. Due to the significant role of forward contracts for market power mitigation and risk hedging in power markets, the impacts of these contracts on capacity withholding are considered. The effects of GenCos’ strategic forward contracts on capacity withholding are also discussed. Moreover, the relationship between capacity withholding of GenCos and market price distortion is acquired. A two-settlement market including a forward market and a spot market is used to describe GenCos’ strategic forward contracting and spot market competition.
A. Zakariazadeh, Sh. Jadid,
Volume 10, Issue 2 (6-2014)
Abstract
Microgrid (MG) is one of the important blocks in the future smart distribution systems. The scheduling pattern of MGs affects distribution system operation. Also, the optimal scheduling of MGs will be result in reliable and economical operation of distribution system. In this paper, an operational planning model of a MG which considers multiple demand response (DR) programs is proposed. In the proposed approach, all types of loads can participate in demand response programs which will be considered in either energy or reserve scheduling. Also, the renewable distributed generation uncertainty is covered by reserve prepared by both DGs and loads. The novelty of this paper is the demand side participation in energy and reserve scheduling, simultaneously. Furthermore the energy and reserve scheduling is proposed for day-ahead and real-time. The proposed model was tested on a typical MG system in connected mode and the results show that running demand response programs will reduce total operation cost of MG and cause more efficient use of resources.
B. Adineh, H. Rajabi Mashhadi, M. E. Hajiabadi,
Volume 10, Issue 2 (6-2014)
Abstract
The main goal of this paper is to structurally analyze impact of DSM programs on reliability indices. A new approach is presented to structurally decompose reliability index Expected Energy Not Supplied (EENS) by using Monte Carlo simulation. EENS is decomposed into two terms. The first term indicates EENS which is caused by generation contingencies. The second term indicates EENS which is caused by transmission and generation contingencies. The proposed approach can be used to indicate appropriate buses for applying DSM. Furthermore, networks are studied at two levels HLI and HLII. Studies show that in some networks reliability indices are affected mostly at the HLI level. While in some other networks, reliability indices are influenced mostly at the HLII level. It means that in these networks, reliability indices are affected by transmission contingencies. Then, it is shown that the implementation of load shifting is effective in some networks and buses. These are the ones which their EENS is more influenced by generation contingencies. However it is not effective in the ones which their EENS is more influenced by transmission contingencies. The simulation results on the IEEE-RTS and Khorasan network show the efficiency of the proposed approach.
Sh. Jadid, S. A. H. Bahreyni,
Volume 10, Issue 4 (12-2014)
Abstract
Smart Grids are result of utilizing novel technologies such as distributed energy resources, and communication technologies in power system to compensate some of its defects. Various power resources provide some benefits for operation domain however, power system operator should use a powerful methodology to manage them. Renewable resources and load add uncertainty to the problem. So, independent system operator should use a stochastic method to manage them. A Stochastic unit commitment is presented in this paper to schedule various power resources such as distributed generation units, conventional thermal generation units, wind and PV farms, and demand response resources. Demand response resources, interruptible loads, distributed generation units, and conventional thermal generation units are used to provide required reserve for compensating stochastic nature of various resources and loads. In the presented model, resources connected to distribution network can participate in wholesale market through aggregators. Moreover, a novel three-program model which can be used by aggregators is presented in this article. Loads and distributed generation can contract with aggregators by these programs. A three-bus test system and the IEEE RTS are used to illustrate usefulness of the presented model. The results show that ISO can manage the system effectively by using this model
S. G. M. Rokni, M. Radmehr, A. Zakariazadeh,
Volume 15, Issue 1 (3-2019)
Abstract
In this paper, a new energy management method is proposed for residential consumers based on a distributed algorithm. Consumers could participate in demand response programs by managing their schedulable and deferrable loads as well as using of photovoltaic (PV) systems. In the proposed method, the Alternating Direction Method of Multiplier (ADMM) is used to model the distributed management and scheduling of buildings electricity consumption. By implementing the distributed algorithm, a large number of residential consumers can update their consumption parameters by online communication with the central controller in parallel. The results confirm that residential customers are able to reduce their electricity bill by modifying their electricity consumption patterns without reducing their welfare.
M. Aghamohamadi, M. Samadi, M. Pirnahad,
Volume 15, Issue 1 (3-2019)
Abstract
The integration of different energy types and new technological advances in multi-energy infrastructures, enable energy hubs (EH) to supply load demands at a lower cost which may affect the price responsive loads, since the energy could be offered with a lower price at the EH output ports, compared to the upstream energy markets. In this paper a new EH operation model is proposed by which the optimal responsive load modifications against the obtained EH output energy prices as well as the EH schedules are determined. To achieve this goal, a tri-step approach is proposed. At the first step the EH output energy prices are obtained for each energy type in each hour of the scheduling horizon. These energy prices are based on the EH hourly operation and would change as the EH operation changes. At the second step, the optimal responsive load modifications against the obtained EH output energy prices are simulated using the new proposed integrated responsive load model which is capable to model the price responsive loads in multi-energy systems for any type of energy carrier. Since, any changes in load demand (due to its responsiveness) can jeopardize the EH power balance constraint, the obtained EH operation would be infeasible, considering the new modified load pattern. To cope with this interdependency, a new iterative methodology is proposed at the third step in which, the EH optimal operation + EH output energy price determination + responsive load modification is implemented in a loop till the 24 hour aggregated load modification becomes lower than the pre-determined convergence tolerance. Based on the obtained results from solving the proposed methodology through a comprehensive case study, the aggregated supplied energy has been increased by 7.3%, while, the customers payments has reduced by 14.6%. Accordingly, the customer’s satisfaction has increased.
A. Mohammadi, S. Soleymani, B. Mozafari, H. Mohammadnezhad-Shourkaei,
Volume 17, Issue 2 (6-2021)
Abstract
This paper proposes an advanced distribution automation planning problem in which emergency-based demand response plans are incorporated during service restoration process. The fitness function of this planning problem consists of various costs associated with fault occurrence in electric distribution systems consisting of the total yearly cost of customers’ interruptions, the total annualized investment cost of control and protection devices deployment, including sectionalizing switches, circuit breakers, and fuses and the total annual cost of performing emergency-based demand response programs in the service restoration process. Moreover, the customers’ behavior in participating in the service restoration process is also modeled through using an S-function. The proposed advanced distribution automation planning method is implemented on the fourth bus of the Roy Bilinton test system in order to evaluate its efficacy. The obtained results show that the reliability indices and the total cost of distribution automation are reduced by about 9% and 12% more than the published methods for distribution automation, respectively.
T. Agheb, I. Ahmadi, A. Zakariazadeh,
Volume 17, Issue 3 (9-2021)
Abstract
Optimal placement and sizing of distributed renewable energy resources (DER) in distribution networks can remarkably influence voltage profile improvement, amending of congestions, increasing the reliability and emission reduction. However, there is a challenge with renewable resources due to the intermittent nature of their output power. This paper presents a new viewpoint at the uncertainties associated with output powers of wind turbines and load demands by considering the correlation between them. In the proposed method, considering the simultaneous occurrence of real load demands and wind generation data, they are clustered by use of the k-means method. At first, the wind generation data are clustered in some levels, and then the associated load data of each generation level are clustered in several levels. The number of load levels in each generation level may differ from each other. By doing so the unrealistic generation-load scenarios are omitted from the process of wind turbine sizing and placement. Then, the optimum sizing and placement of distributed generation units aiming at loss reduction are carried out using the obtained generation-load scenarios. Integer-based Particle Swarm Optimization (IPSO) is used to solve the problem. The simulation result, which is carried out using MATLAB 2016 software, shows that the proposed approach causes to reduce annual energy losses more than the one in other methods. Moreover, the computational burden of the problem is decreased due to ignore some unrealistic scenarios of wind and load combinations.
Nasreddine Attou, Sid-Ahmed Zidi, Samir Hadjeri, Mohamed Khatir,
Volume 19, Issue 3 (9-2023)
Abstract
Demand-side management has become a viable solution to meet the needs of the power system and consumers in the past decades due to the problems of power imbalance and peak demand on the grid. This study focused on an improved decision tree-based algorithm to cover off-peak hours and reduce or shift peak load in a grid-connected microgrid using a battery energy storage system (BESS), and a demand response scheme. The main objective is to provide an efficient and optimal management strategy to mitigate peak demand, reduce the electricity price, and replace expensive reserve generation units. The developed algorithm is evaluated with two scenarios to see the behavior of the management system throughout the day, taking into account the different types of days (weekends and working days), the random profile of the users' demand, and the variation of the energy price (EP) on the grid. The simulation results allowed us to reduce the daily consumption by about 30% to 40% and to fill up to 12% to 15% of the off-peak hours with maximum use of renewable energies, demonstrating the control system's performance in smoothing the load curve.
Hamid Karimi,
Volume 20, Issue 1 (3-2024)
Abstract
This paper proposes a stochastic optimization problem for local integrated hydrogen-power energy systems. In the proposed model, the integrated system tries to reduce the day-ahead operation costs using dispatchable resources, renewable energy resources, battery energy storage systems, demand response programs, and energy trading with the upstream network. Also, the integrated system is able to transact electricity with the upstream network to get more benefits. When the generation of renewable resources is high, the integrated system can convert the surplus electricity to hydrogen by power-to-gas units. The generated hydrogen can be sold to different industries or stored in the hydrogen tank storage. During peak hours, the stored hydrogen can be imported into the gas-to-power unit to generate the required electricity. The sector coupling between electricity and hydrogen provides more flexibility for integrated systems and is an effective solution to control the uncertainty of renewable energy resources in order to increase the power and energy flexibilities. The simulation results show that the proposed sector coupling provides the opportunity for electricity and hydrogen trading for integrated system. The benefit of the integrated system by electricity and hydrogen trading with the upstream network and different industries are $ 88.39, and $ 6846, respectively.
