154 research outputs found
A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies
In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions
Day-Ahead Network-Constrained Scheduling of CHP and Wind Based Energy Systems Integrated with Hydrogen Storage Technology
The integration of renewable energy sources is vastly increased in recent decades considering environmental concerns and lack of fossil fuels. Such integration has appeared novel challenges in electrical energy systems according to their uncertain nature. The hydrogen energy storage (HES) system plays a significant role is power systems by converting extra wind power to the hydrogen using power to hydrogen (P2H) technology. In addition, the emerging technologies such as combined heat and power (CHP) units are effective in increasing the efficiency of power systems. This work presents a day-ahead scheduling scheme for CHP-HES based electrical energy networks with high integration of wind power sources. The effectiveness of the presented model is investigated by implementation on the IEEE 6-bus system. The impact of heat load increment has been studied on scheduling of generation plants, wind power dispatch and operation cost of the system. The simulation results prove that operation cost of the system and wind power curtailment have been decreased using the HES technology
Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs
The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads
Two-Stage Stochastic Day-Ahead Market Clearing in Gas and Power Networks Integrated with Wind Energy
DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets
Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy
Network-constrained joint energy and flexible ramping reserve market clearing of power- and heat-based energy systems : a two-stage hybrid IGDT-stochastic framework
This article proposes a new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed
Genetic algorithm for energy commitment in a power system supplied by multiple energy carriers
Evaluation of hydrogen storage technology in risk-constrained stochastic scheduling of multi-carrier energy systems considering power, gas and heating network constraints
The operation of energy systems considering a multi-carrier scheme takes several advantages of economical, environmental, and technical aspects by utilizing alternative options is supplying different kinds of loads such as heat, gas, and power. This study aims to evaluate the influence of power to hydrogen conversion capability and hydrogen storage technology in energy systems with gas, power, and heat carriers concerning risk analysis. Accordingly, conditional value at risk (CVaR)-based stochastic method is adopted for investigating the uncertainty associated with wind power production. Hydrogen storage system, which can convert power to hydrogen in off-peak hours and to feed generators to produce power at on-peak time intervals, is studied as an effective solution to mitigate the wind power curtailment because of high penetration of wind turbines in electricity networks. Besides, the effect constraints associated with gas and district heating network on the operation of the multi-carrier energy systems has been investigated. A gas-fired combined heat and power (CHP) plant and hydrogen storage are considered as the interconnections among power, gas and heat systems. The proposed framework is implemented on a system to verify the effectiveness of the model. The obtained results show the effectiveness of the model in terms of handling the risks associated with multi-carrier system parameters as well as dealing with the penetration of renewable resources
Evaluating the Impact of Multi-Carrier Energy Storage Systems in Optimal Operation of Integrated Electricity, Gas and District Heating Networks
Various energy networks such as electricity, natural gas, and district heating can be connected by emerging technologies for efficient application of renewable energy sources. On the other hand, the pressure shortage in the natural gas network and increasing heat loss in the district heating network by growth of gas and heat load in winter might play a significant role in the participation of combined heat and power units in the energy markets and operation cost of the whole integrated energy system. Hence, this paper presents a multi-network constrained unit commitment problem in the presence of multi-carrier energy storage technologies aiming to minimize the operation cost of an integrated electricity, gas and district heating system while satisfying the constraints of all three networks. In addition, an information gap decision theory is developed for studying the uncertainty of energy sources under risk-seeker and risk-averse strategies with no need for probability distribution function. Moreover, the role of multi-carrier energy storage technologies in integrated networks is investigated, which indicates decrement of total operation cost and reduction of the effect of wind power uncertainty on total operation cost in presence of the storage technologies
Hourly Price-Based Demand Response for Optimal Scheduling of Integrated Gas and Power Networks Considering Compressed Air Energy Storage
Gas-fired plants are becoming an optimal and practical choice for power generation in electricity grids due to high efficiency and less emissions. Such plants with fast start-up capability and high ramp rate are flexible in response to stochastic load variations. Meanwhile, gas system constraints affect the flexibility and participation of such units in the energy market. Compressed air energy storage (CAES) as a flexible source with high ramp rate can be an alternative solution to reduce the impact of gas system constraints on the operation cost of a power system. In addition, demand response (DR) programs are expressed as practical approaches to overcome peak-demand challenges. This study introduces a stochastic unit commitment scheme for coordinated operation of gas and power systems with CAES technology as well as application of an hourly price-based DR. The introduced model is performed on a six-bus system with a six-node gas system to verify the satisfactory performance of the model
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