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.     

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

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.     

Modelling electricity storage systems management under the influence of demand‐side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market‐clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off‐peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand‐side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off‐peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off‐peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable. Copyright © 2008 John Wiley & Sons, Ltd.     

Energy‐cost‐aware flow shop scheduling considering intermittent renewables,energy storage,and real‐time electricity pricing

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.     

The effects of vehicle‐to‐grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug‐in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle‐to‐grid power. The ability of vehicle‐to‐grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug‐in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.     

Balancing power supply and demand in remote off‐grid regions by means of a novel micro‐scale combined feedstock biomass generation plant

Providing electricity to a group of remote domestic or industrial customers can be achieved by a grid connection, or by an off‐grid (island) generator. While the former can become costly and will likely be prone to disruption, the latter is normally based on fossil fuels, which makes fuel sourcing and transport critical. To overcome these obstacles, a novel micro‐scale biomass generation plant was developed. This plant uses locally available renewable biomass feedstock to generate decentralized power at the point of demand and without the necessity of a grid connection. In this paper, load simulations on the basis of a process simulation model of the plant are performed to achieve a continuous match of supply and demand. It is analysed which load characteristics and fluctuations have to be expected when generating for a remote group of domestic customers, and it is evaluated how the plant needs to be operated to always provide sufficient power. Additionally, the fuel storage system of the plant system is investigated: The plant does not employ electrical storage, but instead matches demand and supply by means of internal usage of heat and power and through fuel storage. Relative and absolute storage levels as well as the storage charge/discharge cycles are analysed, and it will be shown that the plant can easily accommodate severe load fluctuations. Finally, the plant load factors are evaluated, and the findings show that this design is an interesting alternative to common island generators or to a conventional grid connection for remote customers. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

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.     

Review of barriers to the introduction of residential demand response: a case study in the Netherlands

Demand response, defined as the shifting of electricity demand, is generally believed to have value both for the grid and for the market: by matching demand more closely to supply, consumers could profit from lower prices, while in a smart grid environment, more renewable electricity can be used and less grid capacity may be needed. However, the introduction of residential demand response programmes to support the development of smart grids that includes renewable generation is hampered by a number of barriers. This paper reviews these barriers and categorises them for different demand programmes and market players. The case study for the Netherlands shows that barriers can be country specific. Two types of demand response programmes have been identified as being the most promising options for households in smart grids: price‐based demand response and direct load control, while they may not be beneficial for market players or distribution system operators. © 2016 The Authors. International Journal of Energy Research Published by John Wiley & Sons Ltd.     

Future security of power supply in Germany—The role of stochastic power plant outages and intermittent generation

Many power plants in Germany and Europe are approaching the end of their technical lifetime. Moreover, the increasing wind and solar power generation reduces the operation times of thermal power plants, making future investments in new generation capacity uncertain under current market conditions. Consequently, the future development of security of power supply is unclear. In this paper, we assess the impact of stochastic fluctuations in power plant availability, renewable generation, and grid load on the future security of supply in Germany. We model variations in power plant availability by application of a combined Mean‐reversion Jump‐diffusion approach. On the basis of that and using Monte‐Carlo methods, we simulate 300 different time series of availability. These profiles are fed into the fundamental power system model REMix, applied to evaluate the appearance of supply shortfalls in hourly resolution. We assess 6 scenarios for the year 2025, differing in renewable generation and demand profiles, as well as grid infrastructure. Geographical focus of the analysis is Germany, but the electricity exchange with its European neighbours is modelled as well. Our results show that the choice of the power plant availability profile can change the loss of load expectation and loss of load hours by up to 50%. However, the influence of load and renewable generation profiles is found to be significantly higher. Assuming that no new conventional power plants are built and existing plants are decommissioned at the end of their empirical lifetime, we identify supply gaps of up to 2.7 GW in Germany.     

Exploring the hypothetical limits to a nuclear and renewable electricity future

This article evaluates whether the world can transition to a future global electricity system powered entirely by nuclear power plants, wind turbines, solar panels, geothermal facilities, hydroelectric stations, and biomass generators by 2030. It begins by explaining the scenario method employed for predicting future electricity generation, drawn mostly from tools used by the International Energy Agency. The article projects that the world would need to build about 7744 Gigawatts (GW) of installed electricity capacity by 2030 to provide 37.2 thousand terawatt‐hours (TWh). Synthesizing data from the primary literature, the article argues that meeting such a projection with nuclear and renewable power stations will be difficult. If constructed using commercially available and state‐of‐the‐art nuclear and renewable power stations today, the capital cost would exceed $40 trillion, anticipated negative externalities would exceed $1 trillion per year, and immense strain would be placed on land, water, material, and human resources. Even if nuclear and renewable power technologies were much improved, trillions of dollars of investment would still be needed, millions of hectares of land set aside, quadrillions of gallons of water used, and material supplies of aluminum, concrete, silicon, and steel heavily utilized or exhausted. Because of these constraints, the only true path towards a more sustainable electricity system appears to be reducing demand for electricity and consuming less of it. Copyright © 2009 John Wiley & Sons, Ltd.     

Potential of renewable surplus electricity for power-to-gas and geo-methanation in Switzerland

Energy systems are increasingly exposed to variable surplus electricity from renewable sources, particularly photovoltaics. This study estimates the potential to use surplus electricity for power-to-gas with geo-methanation for Switzerland by integrated energy system and power-to-gas modelling. Various CO₂ point sources are assessed concerning exploitable emissions for power-to-gas, which were found to be abundantly available such that 60 TWh surplus electricity could be converted to methane, which is the equivalent of the current annual Swiss natural gas demand. However, the maximum available surplus electricity is only 19 TWh even in a scenario with high photovoltaic expansion. Moreover, making this surplus electricity available for power-to-gas requires an ideal load shifting capacity of up to 10 times the currently installed pumped-hydro capacity. Considering also geological and economic boundary conditions for geo-methanation at run-of-river and municipal waste incinerator sites with nearby CO₂ sources reduces the exploitable surplus electricity from 19 to 2 TWh.     

Alternative Electricity Generation Opportunities

Abstract

In this article, comparing four renewable energy sources shows 70% of the electricity generated by the four to come from geothermal with only 42% of the total installed capacity. Wind energy contributes 27% of the electricity, but has 52% of the installed capacity. Solar energy produces 2% of the electricity and tidal energy 1%. Biomass constitutes 93% of the total direct heat production from renewables, geothermal 5%, and solar heating 2%. Conventional fossil energy will not be enough to meet the continuously increasing need for energy in the future. In this case, renewable energy sources will become important. Alternative energy sources are increasing need for energy in the future.     

Power characteristics of a fuel cell micro‐grid with wind power generation

An independent micro‐grid connected with renewable energy has the potential to reduce energy costs, and reduce the amount of greenhouse gas discharge. However, the frequency and voltage of a micro‐grid may not be stable over a long time due to the input of unstable renewable energy, and changes in short‐period power load that are difficult to predict. Thus, when planning the installation of a micro‐grid, it is necessary to investigate the dynamic characteristics of the power. About the micro‐grid composed from 10 houses, a 2.5 kW proton exchange membrane fuel cell is installed in one building, and it is assumed that this fuel cell operated corresponding to a base load. A 1 kW PEM‐FC is installed in other seven houses, in addition a 1.5 kW wind turbine generator is installed. The micro‐grid to investigate connects these generating equipments, and supplies the power to each house. The dynamic characteristics of this micro‐grid were investigated in numerical analysis, and the cost of fuel consumption and efficiency was also calculated. Moreover, the stabilization time of the micro‐grid and its dynamic characteristics accompanied by wind‐power generation and fluctuation of the power load were studied. Copyright © 2007 John Wiley & Sons, Ltd.     

A 100% renewable electricity generation system for New Zealand utilising hydro,wind, geothermal and biomass resources

The New Zealand electricity generation system is dominated by hydro generation at approximately 60% of installed capacity between 2005 and 2007, augmented with approximately 32% fossil-fuelled generation, plus minor contributions from geothermal, wind and biomass resources. In order to explore the potential for a 100% renewable electricity generation system with substantially increased levels of wind penetration, fossil-fuelled electricity production was removed from an historic 3-year data set, and replaced by modelled electricity production from wind, geothermal and additional peaking options. Generation mixes comprising 53–60% hydro, 22–25% wind, 12–14% geothermal, 1% biomass and 0–12% additional peaking generation were found to be feasible on an energy and power basis, whilst maintaining net hydro storage. Wind capacity credits ranged from 47% to 105% depending upon the incorporation of demand management, and the manner of operation of the hydro system. Wind spillage was minimised, however, a degree of residual spillage was considered to be an inevitable part of incorporating non-dispatchable generation into a stand-alone grid system. Load shifting was shown to have considerable advantages over installation of new peaking plant. Application of the approach applied in this research to countries with different energy resource mixes is discussed, and options for further research are outlined.     

Transforming the electricity generation of the Berlin–Brandenburg region,Germany

We present possible steps for Germany's capital region for a pathway towards high-level renewable energy contributions. To this end, we give an overview of the current energy policy and status of electricity generation and demand of two federal states: the capital city Berlin and the surrounding state of Brandenburg. In a second step we present alternative, feasible scenarios with focus on the years 2020 and 2030. All scenarios were numerically evaluated in hourly time steps using a cost optimisation approach. The required installed capacities in an 80% renewables scenario in the year 2020 consist of 8.8 GW wind energy, 4.8 GW photovoltaics, 0.4 GW_el bioenergy, 0.6 GW_el methanation and a gas storage capacity of 180 GWh_th. In order to meet a renewable electricity share of 100% in 2030, approximately 9.5 GW wind energy, 10.2 GW photovoltaics and 0.4 GW_el bioenergy will be needed, complemented by a methanation capacity of about 1.5 GW_el and gas storage of about 530 GWh_th. In 2030, an additional 11 GWh_el of battery storage capacity will be required. Approximately 3 GW of thermal gas power plants will be necessary to cover the residual load in both scenarios. Furthermore, we studied the transmission capacities of extra-high voltage transmission lines in a second simulation and found them to be sufficient for the energy distribution within the investigated region.     

考虑源荷不确定性的分时电价动态修正机制研究

针对新型电力系统中可再生能源出力及负荷需求的不确定性造成源荷协调困难,导致难以制定合理的分时电价的问题,该文提出一种考虑源荷不确定性的分时电价动态修正机制。首先,根据可再生能源出力的波动性以及不确定性,建立新能源并网功率与并网电量偏差量化模型;其次,根据需求侧负荷的变化特征,结合可再生能源出力不确定性,通过多种不确定性因素影响条件的误差计算方法,建立电价概率密度模型。然后,根据负荷上报的用电量以及预报电价,建立考虑源荷不确定性的电力市场分时电价动态修正与优化模型,并采用粒子群算法进行模型求解。最后,通过实际运行数据仿真验证该文所提方法的有效性。     

Load management in municipal electricity systems

Load management is one means of reducing maximum electricity load, and hence also the cost of electricity. In Sweden, the amount charged during the maximum load hour might be about 200 times higher than the standard charge for one kilowatt-hour. If the load could be reduced by certain equipment in factories and buildings, the need for new power stations and higher capacity in the grid would also be decreased. Using electricity load data for one full year and a short computer program, this paper shows by how much the load could be reduced by postponing demand. If part of the load could be postponed by only one hour, this part may need to be only very small for maximum benefit. If longer time segments were practicable, larger chunks could be transferred. The main result of the study is, however, that load management in practice is a very subtle task if an optimal solution is to be achieved. © 1997 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.     

Financing solar thermal technologies under DSM programs: an innovative approach to promote renewable energy

A wide range of demand side management (DSM) options has been practiced so far in developed as well as in developing countries. However, solar thermal technologies have been left out from DSM programs considering them as supply side options. This study argues that a number of solar thermal technologies, which provide the same services as electric appliances, can be considered as DSM options and examines the possibility of promoting solar water heaters (SWH) under DSM programs in Thailand. The study found that installation of SWH in place of conventional electric water heaters (EWH) to meet hot water demand in the residential sector would be economically beneficial to the country as a whole. However, switching to SWH from EWH would be unlikely without having government interventions as there would be no incentives to individual consumers in doing so. If the government or state electric utilities provide funding to residential consumers through DSM programs for replacing their EWH by SWH, the total electricity generation in Thailand during the 2000–2015 period would decrease by 3.8 per cent. Moreover, promotion of SWH under DSM programs would cause 3.35 and 1.41 per cent reductions of total power sector CO₂‐ and NO_x‐emissions respectively, during the same period. This study also reveals that solar thermal technologies, especially the SWH, could be better options for DSM programs compared to the end‐use efficiency improvement options in Thailand. Copyright © 2000 John Wiley & Sons, Ltd.     

Sizing and operating power-to-gas systems to absorb excess renewable electricity

Various configurations of power-to-gas system are investigated as a means for capturing excess wind power in the Emden region of Germany and transferring it to the natural gas grid or local biogas-CHP plant. Consideration is given to producing and injecting low concentration hydrogen admixtures, synthetic methane, or hydrogen/synthetic methane mixtures. Predictions based on time series data for wind generation and electricity demand indicate that excess renewable electricity levels will reach about 40 MW and 45 GW h per annum by 2020, and that it is desirable to achieve a progression in power-to-gas capacity in the preceding period. The findings are indicative for regions transitioning from medium to high renewable power penetrations. To capture an increasing proportion of the growing amount of excess renewable electricity, the following recommendations are made: implement a 4 MW hydrogen admixture plant and hydrogen buffer of 600 kg in 2018; then in 2020, implement a 17 MW hybrid system for injecting hydrogen and synthetic methane (with a hydrogen storage capacity of at least 400 kg) in conjunction with a bio-methane injection plant. The 17 MW plant will capture 68% of the available excess renewable electricity in 2020, by offering an availability to the electricity grid operator of >97% and contributing 19.1 GW h of ‘green’ gas to the gas grid.