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1.
Energy‐cost‐aware flow shop scheduling considering intermittent renewables,energy storage,and real‐time electricity pricing
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The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables. 相似文献
2.
Model‐based approach for planning renewable energy transition in a resource‐constrained electricity system—A case study from India
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Globally, electricity systems are going through transitions. The contributions from renewable energy‐based power generation, both in installed capacity and electricity generation, are moving from marginal to the mainstream. India is not an exception; it is aggressively pursuing this transition by fixing steep targets for renewable capacity additions. While the cost of renewable energy sources is expected to fast reach grid parity, the policy interventions play a critical role in ramping up the efforts to support the proposed investments in renewable capacity and renewable electricity generation. In this respect, this research attempts to analyze the effectiveness of renewable energy policies such as Renewable Purchase Obligation (RPO) and Renewable Energy Certificate mechanisms in tapping the renewable energy potential in India. We propose a mixed‐integer linear programming model‐based approach to evaluate the effectiveness of the above interventions in the Indian context. The model is developed and validated as a low carbon electricity planning tool to optimally meet the dynamic electricity demand and RPO targets as well as to manage the unmet total electricity demand and RPO targets. The Karnataka state electricity system (a state in south India) is chosen as a case study. The results suggest that Karnataka Electricity System is moving toward a sustainable renewable energy future even without any support from nonsolar Renewable Energy Certificate policy. However, policy interventions are critical for optimally utilizing the solar generation capacity. 相似文献
3.
Supporting security and adequacy in future energy systems: The need to enhance long‐term energy system models to better treat issues related to variability
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Manuel Welsch Mark Howells Mohammad Reza Hesamzadeh Brian Ó Gallachóir Paul Deane Neil Strachan Morgan Bazilian Daniel M. Kammen Lawrence Jones Goran Strbac Holger Rogner 《国际能源研究杂志》2015,39(3):377-396
As the shares of variable renewable generation in power systems increase, so does the need for, inter alia, flexible balancing mechanisms. These mechanisms help ensure the reliable operation of the electricity system by compensating for fluctuations in supply or demand. However, a focus on short‐term balancing is sometimes neglected when assessing future capacity expansions with long‐term energy system models. Developing heuristics that can simulate short‐term system issues is one way of augmenting the functionality of such models. To this end, we present an extended functionality to the Open Source Energy Modelling System (OSeMOSYS), which captures the impacts of short‐term variability of supply and demand on system adequacy and security. Specifically, we modelled the system adequacy as the share of wind energy is increased. Further, we enable the modelling of operating reserve capacities required for balancing services. The dynamics introduced through these model enhancements are presented in an application case study. This application indicates that introducing short‐term constraints in long‐term energy models may considerably influence the dispatch of power plants, capacity investments, and, ultimately, the policy recommendations derived from such models. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
4.
Brian H. Bowen Devendra CanchiVishal Agarwal Lalit Paul V. PreckelF.T. Sparrow Marty W. Irwin 《Energy Policy》2010
The Purdue Long-Term Electricity Trading and Capacity Expansion Planning Model simultaneously optimizes both transmission and generation capacity expansions. Most commercial electricity system planning software is limited to only transmission planning. An application of the model to India's national power grid, for 2008–2028, indicates substantial transmission expansion is the cost-effective means of meeting the needs of the nation's growing economy. An electricity demand growth rate of 4% over the 20-year planning horizon requires more than a 50% increase in the Government's forecasted transmission capacity expansion, and 8% demand growth requires more than a six-fold increase in the planned transmission capacity expansion. The model minimizes the long-term expansion costs (operational and capital) for the nation's five existing regional power grids and suggests the need for large increases in load-carrying capability between them. Changes in coal policy affect both the location of new thermal power plants and the optimal pattern inter-regional transmission expansions. 相似文献
5.
Impact of electric vehicles on a future renewable energy‐based power system in Europe with a focus on Germany
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Thomas Pregger 《国际能源研究杂志》2018,42(8):2670-2685
Electric mobility is expected to play a key role in the decarbonisation of the energy system. Continued development of battery electric vehicles is fundamental to achieving major reductions in the consumption of fossil fuels and of CO2 emissions in the transport sector. Hydrogen can become an important complementary synthetic fuel providing electric vehicles with longer ranges. However, the environmental benefit of electric vehicles is significant only if their additional electricity consumption is covered by power production from renewable energy sources. Analysing the implications of different scenarios of electric vehicles and renewable power generation considering their spatial and temporal characteristics, we investigate possible effects of electric mobility on the future power system in Germany and Europe. The time horizon of the scenario study is 2050. The approach is based on power system modelling that includes interchange of electricity between European regions, which allows assessing long‐term structural effects in energy systems with over 80% of renewable power generation. The study exhibits strong potential of controlled charging and flexible hydrogen production infrastructure to avoid peak demand increases and to reduce the curtailment of renewable power resulting in reduced system operation, generation, and network expansion costs. A charging strategy that is optimised from a systems perspective avoids in our scenarios 3.5 to 4.5 GW of the residual peak load in Germany and leads to efficiency gains of 10% of the electricity demand of plug‐in electric vehicles compared with uncontrolled loading. 相似文献
6.
The power system is expected to play an important role in climate change mitigation. Variable renewable energy (VRE) sources, such as wind and solar power, are currently showing rapid growth rates in power systems worldwide, and could also be important in future mitigation strategies. It is therefore important that the electricity sector and the integration of VRE are correctly represented in energy models. This paper presents an improved methodology for representing the electricity sector in the long-term energy simulation model TIMER using a heuristic approach to find cost optimal paths given system requirements and scenario assumptions. Regional residual load duration curves have been included to simulate curtailments, storage use, backup requirements and system load factor decline as the VRE share increases. The results show that for the USA and Western Europe at lower VRE penetration levels, backup costs form the major VRE cost markup. When solar power supplies more than 30% of the electricity demand, the costs of storage and energy curtailments become increasingly important. Storage and curtailments have less influence on wind power cost markups in these regions, as wind power supply is better correlated with electricity demand. Mitigation scenarios show an increasing VRE share in the electricity mix implying also increasing contribution of VRE for peak and mid load capacity. In the current scenarios, this can be achieved by at the same time installing less capital intensive gas fired power plants. Sensitivity analysis showed that greenhouse gas emissions from the electricity sector in the updated model are particularly sensitive to the availability of carbon capture and storage (CCS) and nuclear power and the costs of VRE. 相似文献
7.
S. Obara 《国际能源研究杂志》2006,30(13):1055-1073
The chromosome model showing system operation pattern is applied to GA (genetic algorithm), and the method of optimization operation planning of energy system is developed. The optimization method of this operation planning was applied to the compound system of methanol‐steam‐reforming‐type fuel cell, geothermal heat pump and the electrolysis tank of water. The operation planning was performed for the energy system using the energy demand pattern of the individual residence of Sapporo city. From analysis results, the amount of outputs of a solar module and the relation of the operation cost of the system, which are changed by the weather were clarified. The representation day in February of the ratio of the operation cost in case of (0% of output rates) the rainy weather to the time of fine weather (100% of output rates) is 1.12. And the representation day in July is 1.71. Furthermore, the optimal capacity of accumulation of electricity and thermal storage was estimated, and they are 308 and 23 MJ, respectively. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
8.
The need for local energy planning is not reduced after liberalization. Both integrated energy companies and local governments have to consider alternative solutions across traditional supply and demand sectors and make plans for the total integrated energy infrastructure. This situation has created a need for new improved methodologies and tools for system planning and operation that include multiple energy carriers and sufficient topological details. In this paper, a novel optimisation model ‘eTransport’ is presented that takes into account both the topology of multiple energy infrastructures and the technical and economic properties of different investment alternatives. The model minimises total energy system cost (investments, operation and emissions) of meeting predefined energy demands of electricity, gas, space heating and tap water heating within a geographical area over a given planning horizon, including alternative supply infrastructures for multiple energy carriers. The model employs a nested optimisation, calculating both the optimal diurnal operation of the energy system and the optimal expansion plan typically 20–30 years into the future. The model is tested on a number of real case studies, and a full graphical user interface has been implemented. A sample case study is included to demonstrate the use of the model. 相似文献
9.
This paper mainly studies the multi‐objective optimization of load dispatch of power systems including renewable energy and CO2 capture and storage (CCS) technologies. The improved environmental/economic load dispatch model for the power system is constructed, considering the renewable energy utilization and CCS technologies. A novel singular weighted method (SWM) has been proposed in this paper for solving this kind of multi‐objective and multi‐constraint optimization problem. A power system with five generators has been applied in one case study to test the model and SWM. It was concluded that the share that each unit takes is not linear; however, the optimal results are largely relevant to the characteristics of the units. In addition, the research results showed that with the increment of the weight coefficient for a certain objective function, the optimization result was closer to the single optimization result for that objective function; and with the increase of forecast demand load, a 35 MW wind energy unit and a 200 MW water energy unit should be built. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
10.
Annelie Carlson 《国际能源研究杂志》2002,26(12):1103-1115
The production of heat and electricity can cause large environmental impacts and, hence, large costs for society. Those are costs that are seldom taken into consideration. An important question is how the future technical energy systems should be formed if environmental costs were considered as any other good or service, such as raw material, capital and labour. This study comprises cost‐effective technical measures when monetary values of external effects are included in an energy system analysis. It is an analysis of how the present energy system can for society be cost‐effectively reconstructed to be more sustainable. A regional energy system model has been developed to perform the study and it concentrates upon production of heat in single‐family houses, multi‐dwelling buildings, non‐residential premises and district heating systems. The analysis adopts a business economic perspective, using present prices of energy carriers, and a more socio‐economic perspective, in which external costs are included. The result of the analysis is the optimal mix of energy carriers as well as new and existing heating plants that minimizes the costs of satisfying a demand for heat. The results show that it is profitable to invest in new heating plants fuelled with woody biomass. Furthermore, the external costs arising with satisfying the demand for heat can decrease substantially, 60%, by carrying through with the investments that are cost‐effective according to the institutional rules valid today. When monetary values of external costs are taken into consideration, this number is additional 5‐percentage points lower. It is shown that if environmental costs are included it is more expensive to continue with business as usual than it is to reconstruct and run a more sustainable energy system. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
11.
Andrei Marinescu Adam Taylor Siobhn Clarke Ioan Serban Corneliu Marinescu 《国际能源研究杂志》2019,43(8):3853-3868
The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation. 相似文献
12.
ABSTRACTOptimal energy renovations of apartment buildings in Finland have a great impact on annual energy demand. However, reduction of energy demand does not necessarily translate into similar changes in peak power demand. Four different types of apartment buildings, representing the Finnish apartment building stock, were examined after optimal energy retrofits to see the influence of retrofitting on hourly power demand. Switching from district heating to ground-source heat pumps reduced emissions significantly under current energy mix. However, the use of ground-source heat pumps increased hourly peak electricity demand by 46–153%, compared to district heated apartment buildings. The corresponding increase in electrical energy demand was 30–108% in the peak month of January. This could increase the use of high emission peak power plants and negate some of the emission benefits. Solar thermal collectors and heat recovery systems could reduce purchased heating energy to zero in summer. Solar electricity could reduce median power demand in summer, but had only a little effect on peak power demand. The reduction in peak power demand after energy retrofits was less than the reduction in energy demand. 相似文献
13.
This paper proposes a stochastic scheduling model to determine optimal operation of generation and storage units of a virtual power plant (VPP) for participating in a joint energy and regulation service (RS) market under uncertainty. Beside electricity, the VPP provides required RSs according to the probability of delivery request in the electricity market. A new model for providing RS is introduced in which the dispatchable generation units are financially compensated with their readiness declarations and will be charged/paid for their real‐time down/up regulations. Besides, the VPP sets up incentive price‐quantity curves to benefit from the potential of demand side management in both energy and RS market. Within the model presented here, the VPP consists of two types of generation units: wind turbine and standby diesel generator; the latter is modeled by considering CO2‐emission penalty costs. The given uncertainties are divided into two parts. Firstly, the uncertainties from the energy market price are simulated using information gap decision theory to evaluate the risk‐based resource scheduling for both risk‐taker and risk‐averse VPP. Other uncertainties affecting decision making such as wind turbine generation, load, regulation up/down calling probabilities, and regulation market prices are modeled via scenario trees. Three typical case studies are implemented to validate the performance and effectiveness of the proposed scheduling approach. 相似文献
14.
Argyris G. Kagiannas Thanassis Didis Dimitris Th. Askounis John Psarras 《国际能源研究杂志》2003,27(2):173-186
The needs that an energy supply system must meet are constantly changing, due to technological, social and political reasons. Effective energy planning is a dynamic process that is repeated periodically and adjusts to changing conditions. Energy decision makers and planners are no longer able to rely on inductive decision making since they have to investigate the effect of various decision parameters and possible future changes. To help in this process, models have been developed where estimates of future load growth, candidate power plants, fuels and other key factors can be introduced, from which the planners can evaluate decision parameters and the available alternatives. The paper presents the different methodologies and practices that are used by 11 energy models for energy demand forecasting, supply side management and generation expansion planning, demand side management and integrated resource planning. The paper concludes to the presentation of a strategic appraisal of the examined energy models appropriate for energy planning in Mozambique. Three models are proposed for conducting demand forecasting, generation expansion planning and demand side management. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
15.
The share of the renewable energy sources (RES) in the global electricity market is substantially increasing as a result of the commitment of many countries to increase the contribution of the RES to their energy mix. However, the integration of RES in the electricity grid increases the complexity of the grid management due to the variability and the intermittent nature of these energy sources. Energy storage solutions such as batteries offer either short-term storage that is not sufficient or longer period storage that is significantly expensive. This paper introduces an energy management approach which can be applied in the case of power and desalinated water generation. The approach is based on mathematical optimization model which accounts for random variations in demands and energy supply. The approach allows using desalination plants as a deferrable load to mitigate for the variability of the renewable energy supply and water and/or electricity demands. A mathematical linear programming model is developed to show the applicability of this idea and its effectiveness in reducing the impact of the uncertainty in the environment. The model is solved for the real world case of Saudi Arabia. The optimal solution accounts for random variations in the renewable energy supply and water and/or electricity demands while minimizing the total costs for generating water and power. 相似文献
16.
Priority and delay constrained demand side management in real‐time price environment with renewable energy source
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Muhammad Babar Rasheed Nadeem Javaid Ashfaq Ahmad Muhammad Awais Zahoor Ali Khan Umar Qasim Nabil Alrajeh 《国际能源研究杂志》2016,40(14):2002-2021
In smart grid, integration of renewable energy sources such as solar and wind is a challenging task because of their intermittent nature. Most of the existing demand side management techniques are based on day‐ahead pricing or time of use pricing that deviate from real‐time pricing because of unpredictable energy consumption trends and electricity prices. This paper presents opportunistic scheduling algorithms in a real‐time pricing environment based on optimal stopping rule. We classify different users and assign priorities based on energy demand. In order to minimize the electricity bill and appliance waiting time cost, we modify the first come first serve scheduling algorithm. Regarding comfort maximization, priority enable early deadline first scheduling algorithm is proposed, which schedules the appliances based on minimum length of operation time and priority constraints. Simulation results validate the effectiveness of the proposed algorithms in terms of electricity cost reduction and user comfort maximization. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
17.
Generally, it is very difficult to assess the true operating cost of an electrical power unit in the countries where there is little or no operational experience. Since Turkey has no experience on operating a nuclear unit, operating costs of a nuclear unit is uncertain for use in generation expansion planning (GEP). Furthermore, there is a disagreement of whether it is cheap or not. In this study, an acceptable level of operating cost of nuclear units is determined for Turkey's power system. It is aimed to find a numerical value for nuclear operating cost at which nuclear is able to compete with other energy sources. Seven types of units are chosen as candidate units to the power system. Mixed-integer programming (MIP) is used as a mathematical model of generation expansion planning. The model consists of the cost function that minimizes the construction and operating costs and the reliability constraints. Adaptive simulated annealing genetic algorithm (ASAGA) is used for optimization algorithm to determine the types, times, and number of candidate units which meet forecasted demand within a pre-specified reliability criterion over the planning horizon from 2006 to 2025. In the case studies, a high level of nuclear energy operating cost is taken and then the cost is gradually lowered. Optimizations are made for each level of nuclear operating costs within four different scenarios and the quantities of nuclear capacity selected by optimizations are recorded. It is determined that, nuclear energy is able to compete with other energy sources when the operating cost is less than 210$/kWh yr or 2.4cent/kWh. 相似文献
18.
Energy consumption has risen in Malaysia because of developing strategies and increasing rate of population. Depletion of fossil fuel resources, fluctuation in the crude oil prices, and emersion of new environmental problems due to greenhouse gasses effects of fossil fuel combustion have convinced governments to invest in development of power generation based on renewable and sustainable energy (RSE) resources. Recently, power generation from RSE resources has been taken into account in the energy mix of every country to supply the annual electricity demand. In this paper, the scenario of the energy mix of Malaysia and the role of RSE resources in power generation are studied. Major RSE sources, namely biomass and biogas, hydro‐electricity, solar energy, and wind energy, are discussed, focusing more toward the electrical energy demand for electrification. It is found that power generation based on biomass and biogas utilization, solar power generation, and hydropower has enough spaces for more development in Malaysia. Moreover, minihydropower and wind power generation could be effective for rural regions of Malaysia. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
19.
Waqas Ahmad Wattoo Donghan Feng Muhammad Yousif Sohaib Tahir Muhammad Tuoqeer Anwar Muhammad Numan 《国际能源研究杂志》2019,43(10):5447-5459
Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax. 相似文献
20.
A low‐carbon electricity supply for Australia was simulated, and the installed capacity of the electrical grid was optimized by shifting the electricity demand of residential electric water heaters (EWHs). The load‐shifting potential of Australia was estimated for each hour of the simulation period using a nationwide aggregate EWH load model on a 90 × 110 raster grid. The electricity demand of water heaters was shifted from periods of low renewable resource and high demand to periods of high renewable resource and low demand, enabling us to effectively reduce the installed capacity requirements of a 100%‐renewable electricity grid. It was found that by shifting the EWH load by just 1 hour, the electricity demand of Australia could be met using purely renewable electricity at an installed capacity of 145 GW with a capacity factor of 30%, an electricity spillage of 20%, and a generation cost of 15.2 ¢/kWh. A breakdown of the primary energy sources used in our scenario is as follows: 43% wind, 29% concentrated solar thermal power, and 20% utility photovoltaic. Sensitivity analysis suggested that further reduction in installed capacity is possible by increasing the load‐shifting duration as well as the volume and insulation level of the EWH tank. 相似文献