Priority and delay constrained demand side management in real‐time price environment with renewable energy source

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.     

Bi‐objective optimization of a grid‐connected decentralized energy system

Motivated by the increasing transition from fossil fuel–based centralized systems to renewable energy–based decentralized systems, we consider a bi‐objective investment planning problem of a grid‐connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon‐intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation‐optimization algorithm and then adapt multi‐objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi‐objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well‐known measures from the literature.     

A price‐regulated electric vehicle charge‐discharge strategy for G2V,V2H,and V2G

Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future.     

Optimizing 100%‐renewable grids through shifting residential water‐heater load

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.     

基于可避免购电成本的持续负荷转移成本有效性分析

为使实时电价制度下电力供应紧缺时用户可通过可中断负荷合同自觉选择中断用电,避免一定的购电成本,建立了基于可避免成本的总购电成本模型,考虑了可再生能源投资标准约束和输配电容量约束对可避免成本估算的影响,提出逐时可避免成本影响下的持续负荷转移的成本有效性分析模型及年可避免购电成本的计算模型,并分析了持续负荷转移实现的成本效益。     

高铁客运站分布式多能源系统供能协同优化策略研究

为解决传统高铁客运站供能系统中能源利用率较低的问题,以日运行购气费用和购电费用最优为优化目标,以系统运行过程中实时能量平衡为约束条件,以可再生能源出力和吸收式制冷占比为优化变量,建立多能源协同供能的分布式能源系统,并将该模型应用于北方某高铁客运站,分析可再生能源的利用率、制冷系统中可再生能源电出力的电制冷占比以及电网出力的节电率。仿真计算结果表明,分布式能源系统的使用提高了可再生能源的利用率,其中风电机组出力占其出力极限的96.5%,光伏机组出力94.7%;相比于参比系统,分布式能源系统的成本节约率为12.5%;电制冷占比为13%;电网的节电率为53.9%。     

A multivariate coupled economic model study on hydrogen production by renewable energy combined with off-peak electricity

An economic prediction model of hydrogen production from renewable energy complemented with off-peak electricity is developed, and the cost of carbon emission for the off-peak electricity from grid is also considered. The variation of hydrogen cost with the utilization hours of off-peak electricity and the allowable range of off-peak electricity utilization hours under different carbon prices and grid average emission factors are investigated. The results show that it is influenced by the multiplication product of the carbon price and the average carbon emission factor of the grid electricity. The use of off-peak electricity reduces the cost of hydrogen when the multiplication product is less than a critical value, while it causes an increase in the cost of hydrogen when larger than the critical value. Off-peak electricity utilization hours are also constrained by the emission intensity requirement of low-carbon or clean hydrogen. In addition, a generalized multivariate coupled analysis method is developed by investigating single variable sensitivities and decision needs at different stages of the project. The results show that the key coupling variables in the site selection stage are hydrogen price, renewable energy utilization hours and integrated tariff, which is determined by both renewable energy and off-peak grid electricity. In the scheme design stage the key coupling variables are electrolysis energy consumption and unit installed cost. When project parameters are determined along with site specific hydrogen price and renewable energy tariffs, further scheme optimization can be undertaken.     

Technology learning for renewable energy: Implications for South Africa's long-term mitigation scenarios

Technology learning can make a significant difference to renewable energy as a mitigation option in South Africa's electricity sector. This article considers scenarios implemented in a Markal energy model used for mitigation analysis. It outlines the empirical evidence that unit costs of renewable energy technologies decline, considers the theoretical background and how this can be implemented in modeling. Two scenarios are modelled, assuming 27% and 50% of renewable electricity by 2050, respectively. The results show a dramatic shift in the mitigation costs. In the less ambitious scenario, instead of imposing a cost of Rand 52/t CO₂-eq (at 10% discount rate), reduced costs due to technology learning turn renewables into negative cost option. Our results show that technology learning flips the costs, saving R143. At higher penetration rate, the incremental costs added beyond the base case decline from R92 per ton to R3. Including assumptions about technology learning turns renewable from a higher-cost mitigation option to one close to zero. We conclude that a future world in which global investment in renewables drives down unit costs makes it a much more cost-effective and sustainable mitigation option in South Africa.     

Cost-effectiveness of renewable electricity policies

We analyze policies to promote renewable sources of electricity. A portfolio standard (RPS) raises electricity prices and primarily reduces gas-fired generation. A knee of the cost curve exists between 15% and 20% goals for 2020 in our central case, and higher natural gas prices lower the cost of greater reliance on renewables. A renewable energy production tax credit lowers electricity price at the expense of taxpayers, which limits its effectiveness in reducing carbon emissions, and it is less cost-effective at increasing renewables than a portfolio standard. Neither policy is as cost-effective as a cap-and-trade policy for achieving carbon emission reductions.     

智能电网下多时间尺度家庭能量管理优化策略

建立负荷在功率约束与需求响应约束下的激励需求响应模型以及含分布式电源、储能与电动汽车的家庭用电模型,在预测模型多时间尺度能量管理的基础上,以最小化用户自身用电费用与买电功率波动的两层目标函数实时优化调整策略。通过实时调整储电池、电动汽车的充放电,从而保证用户购电满足需求相应的要求。最后采用改进的粒子群算法对多时间尺度目标函数进行求解,并且与原始的粒子群算法进行对比,结果表明所提算法可显著降低用户的用电费用与功率波动。     

Model‐based approach for planning renewable energy transition in a resource‐constrained electricity system—A case study from India

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.     

Potential and use of renewable energy sources in Croatia

The current share of renewable energy sources in electricity production in Croatia is very high, around 50%. Nevertheless it is expected that the share of renewables will have to rise and efficient strategies must be examined and adopted. The Croatian government has recognised the important role renewable energy sources could play in Croatian energy and electricity supply. The most important barrier for a wider deployment of renewables in energy production is their cost which is still above those of conventional energy sources. As the energy market is currently undergoing the process of liberalisation, support mechanisms that are compatible with an open market philosophy must be adopted. The characteristics of the Croatian power system, the expected consumption, growth and possible future role of renewables in energy and electricity production is presented. The current legislative framework relevant for renewables is analysed and discussed.     

On the efficient market diffusion of intermittent renewable energies

Capacity costs of renewable energies have been decreasing dramatically and are expected to fall further, making them more competitive with fossils. Building on an analytically tractable peak-load pricing model, we analyze how intermittency of renewable energies affects the market diffusion that results from these lower costs. In particular, once renewables have become competitive by attaining the same levelized cost of electricity (LCOE) as fossils, the marginal increase in efficient capacities due to a further cost reduction varies substantially. Initially it is small, then it rises, but it falls again once renewable capacities are large enough to satisfy the whole electricity demand at times of high availability. If external costs of fossils are internalized by a Pigouvian tax, then perfect competition leads to efficient investments in renewable and fossil capacities; even though we assume that only a subgroup of consumers can adapt their demand to price fluctuations that are caused by the intermittency of renewables. Moreover, fossils receive a capacity payment through the market for their reliability in serving demand of non-reactive consumers. Maximum electricity prices rise with the share of renewables. If regulators impose a price cap, this initially raises investments in renewables, but the effect may reverse if the share of renewables is large.     

A techno-economic analysis of tidal energy technology

The choice of which type of electrical power generation technology to adopt is driven by a number of factors including: cost of generated electricity; responsiveness of generating plant to demand; security of supply/resource availability; environmental impact; and execution risk. Within these, tidal energy is unique as a renewable technology since it has the capability of providing predictable, firm power contributing to security of supply. This predictability gives tidal energy additional value in a future electricity market. Especially one where stochastic renewable technologies contribute to a sizable component of the power supplied; and where reserve capacity is required to maintain supply during periods of non-availability. In the shorter term, in order for tidal energy to gain commercial acceptance, tidal technologies under development need to produce electricity at a competitive price. This paper examines the drivers influencing electricity pricing; current tidal energy developments, aimed at reducing capital costs; and bench-mark these against offshore wind.     

德国新能源和可再生能源立法模式及其对我国的启示

在欧盟新能源和可再生能源立法的强力推动下,德国针对电力、交通和供热供冷等不同领域进行新能源和可再生能源立法,其中电力领域的立法进行得尤为深入;德国制定的2020年交通领域可再生能源利用目标为17%;2020年可再生能源在供热领域最终能源消费总额中的比例将达到14%。在出台各种法律的同时,德国还制定出新能源和可再生能源法规,典型例子是海上风电和生物质发电。德国新能源和可再生能源相关法律法规健全,其中电力和燃气产业、节能和能效以及气候变化三大领域的法律法规尤为重要。作为一个对化石能源有着巨大需求的国家,德国的二氧化碳减排进行得卓有成效,2007年较之于1990年,二氧化碳排放总量下降17.5%,人均二氧化碳排放量下降19%,一次能源供应和经济产出的碳排放强度分别下降10.9%和37.2%。同时还带动了欧盟的二氧化碳减排。相比于德国,我国新能源和可再生能源电力立法存在明显缺陷,一是缺乏深度,二是缺乏定力,三是缺乏耐心。德国新能源和可再生能源立法模式给予我国的启示是:能源立法应从小处做起,不应避重就轻,能源立法应一揽子化。     

Environmental impact assessment of green energy systems for power supply of electric vehicle charging station

The Electric Vehicle (EV) as a clean alternative to Classic Vehicle that use fossil fuels is promoted as an immediate solution to improve the quality parameters of the environment related to the transport sector. The transition to clean electrified mobility must be considered from the sustainability spectrum, and the planning of a strategy related to the implementation of electric vehicles implies, from the beginning, providing clean energy conditions to go toward a green-to-green paradigm. It should be noted that the successful implementation of the “green electro mobility” concept depends heavily on the green energy supply solutions of green electric vehicle, so Electric Vehicle Charging Stations (EV-CS) should be powered by electricity generation systems based on green resources. This research article has as main objective the environmental impact assessment from the perspective of CO₂ emissions embedded in green stand-alone energy systems and the estimation of the environmental benefits of their implementation in the power supply of EV-CS from the perspective of avoided CO₂ emissions compared to the classic electricity supply grid. The results indicate that the green energy systems represent feasible solutions for the independent energy support of electric vehicle charging stations, being able to supply electricity based on on-site available 100% alternative energy sources. Related to 1 kWh of electricity, the CO₂ emissions embedded in these systems represent on average 11.40% of the CO₂ emissions of the electricity supplied through the grid at European level and on average 7.10% of the CO₂ emissions of the electricity supplied through the grid worldwide. Results also show that the average price of 1kWh of electricity generated by the analyzed systems is 4.3 times higher than the average unit price of the European Union grid energy, but this indicator must be correlated with the kgCO₂/kWh cost savings compared to the electricity production from classic power plants.     

Analysis of renewable energy development to power generation in the United States

Renewable energy resources have historically played a small role for electricity generation in the US. However, concerns such as security of energy supply, limitations and price fluctuations of fossil fuels, and threats of climate changes have encouraged US policy makers to think and debate about diversification strategy in the energy supply and promotion of renewables. The current paper discusses the role of renewable portfolio in the US energy action plan during 2010–2030. A system dynamics model is constructed to evaluate different costs of renewable energy utilization by 2030. Results show that while renewables will create a market with near 10 billion $ worth (in the costs level) in 2030, the total value of renewable energy promotion and utilization in the US will be more than 170 billion $(in the costs level) during 2010–2030.     

A techno‐economic analysis on NaS battery energy storage system supporting peak shaving

The merits of electricity grid in Shanghai and sodium sulfur (NaS) storage techniques situation are introduced. High‐energy NaS battery energy storage system (BESS) is very suitable for peak shaving of electricity grid. A cost–benefit analysis model of NaS BESS is established to study the electricity price mechanism in load shift in the light of an example of NaS BESS in Meisei University. Capacity price, energy price and twofold electricity price mechanism are discussed under the fixed payback period. The results show that twofold electric power price mechanism is fitter for NaS BESS than onefold energy price mechanism while onefold capacity price mechanism is not suitable for NaS BESS. The discharge price of NaS BESS has an advantage over Shanghai's electricity price in industrial and commercial peak periods when its construction cost descends to 1000 yuan kWh^?1. Copyright © 2015 John Wiley & Sons, Ltd.     

A real options evaluation model for the diffusion prospects of new renewable power generation technologies

This study presents a policy planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies. Price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices. Demand for electricity is assumed to be increasingly price-sensitive, as the electricity market deregulation proceeds, reflecting new options of consumers to react to electricity price changes (such as time-of-use pricing, unbundled electricity services, and choice of supplier). The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policy-making, it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging renewable energy technologies.     

Master‐slave game optimization method of smart energy systems considering the uncertainty of renewable energy

As the uncertainty of renewable energy output brings more and more risks to the day‐ahead dispatch of the power grid, an optimization scheduling strategy of a smart energy system based on improved master‐slave game model is proposed. Risk factors related to the uncertainties of renewable energy are introduced into the master‐slave game model. Taking the smart energy system as the leader and the end users as the follower, an optimized operation model of the smart energy system based on the improved master‐slave game model is established, which is transformed into a single‐layer linear programming model according to the Karush‐Kuhn‐Tucher conditions and the duality theorem. The benefits of the system and electric vehicle users in four application scenarios are obtained by the YALMIP algorithm and the sensitivity affecting the economics of the smart energy system is analyzed. The validity of the model is verified by a simulation analysis of actual operation data from the smart energy system in China. The simulation results show that the method proposed in this paper can increase the revenue of the smart energy system by 7%, reduce the risk cost and charging cost of electric vehicle users by 63.92% and 48.34%.