Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

Cost-reflective electricity pricing: Consumer preferences and perceptions

In Australia, residential electricity peak demand has risen steeply in recent decades, leading to higher prices as new infrastructure was needed to satisfy demand. One way of limiting further infrastructure-induced retail price rises is via ‘cost-reflective’ electricity network pricing that incentivises users to shift their demand to non-peak periods. Empowering consumers with knowledge of their energy usage is critical to maximise the potential benefits of cost-reflective pricing. This research consulted residential electricity consumers in three Australian states on their perceptions and acceptance of two cost-reflective pricing scenarios (Time-of-Use and Peak Capacity pricing) and associated technologies to support such pricing (smart meters, in-home displays and direct load control devices). An energy economist presented information to focus groups on the merits and limitations of each scenario, and participants’ views were captured. Almost half of the 53 participants were agreeable to Time-of-Use pricing, but did not have a clear preference for Peak Capacity pricing, where the price was based on the daily maximum demand. Participants recommended further information to both understand and justify the potential benefits, and for technologies to be introduced to enhance the pricing options. The results have implications for utilities and providers who seek to reduce peak demand.     

Households’ self-selection of dynamic electricity tariffs

Offering electricity consumers time-differentiated tariffs may reduce peak consumption if consumers choosing the tariffs are demand responsive. However, one concern is that time-differentiated tariffs may attract consumers who benefit without responding to the price, simply because they have a favourable consumption pattern. It is thus important to understand on which basis consumers choose between tariffs. We model the choice as a function of compensating welfare measures, and use a discrete choice model on data from a residential dynamic pricing experiment. The results indicate that higher demand flexibility will tend to increase the propensity to select dynamic tariffs, while consumption patterns do not influence tariff choice significantly.     

A new approach to congestion pricing in electricity markets: Improving user pays pricing incentives

Electricity pricing has traditionally been based on average cost pricing where consumers pay a ‘flat’ tariff based upon the average cost of production and transportation of electricity. The introduction of new ‘smart’ meters allows electricity providers to differentiate tariffs on the basis of time. Utilising congestion pricing theory, the energy industry has embraced ‘time-of-use’ (ToU) tariffs with a view to more efficiently pricing electricity. This paper demonstrates that pricing as a function of demand variability (reflecting capacity utilisation) is a more appropriate alternative to existing ToU tariffs for more efficiently allocating costs to end users. We call this new alternative pricing model ‘first derivative ratio’ FDR pricing. This new approach to congestion pricing could be applied to markets other than electricity, such as road transportation.     

Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing

This paper studies the dynamic demand for residential electricity in Taiwan employing a monthly panel data set, composed of 19 counties and spanning the period from 2007:01 to 2013:12. The partial adjustment model used addresses the endogeneity of the electricity price that results from the increasing-block pricing. The estimated results show that there is a significant seasonal difference in the demand for electricity between the summer and non-summer periods. Both the adjustment speed and own price elasticity during the summer months are found to be lower than those in the non-summer months due to the hot weather in summer. It is easier for consumers to adjust their electricity consumption in response to the changes in electricity pricing during the non-summer time. The estimated inelastic short-run and long-run income effects show that electricity is a necessity for consumers. Moreover, the controversial electricity-conservation policies are found to be ineffective measures for reducing electricity consumption in Taiwan.     

Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey

China’s residential electricity demand has grown rapidly over the last three decades and given the expected continued growth, demand side management (DSM) can play an important role in reducing electricity demand. By using micro-level data collected from 1450 households in 27 provinces in the first-ever China Residential Energy Consumption Survey in 2012, this study estimates the effects of three DSM measures empirically: tiered household electricity pricing, China Energy Label program, and information feedback mechanisms. We find these measures have contributed to moderating residential electricity demand growth but additional policy reform and tools are needed to increase their effectiveness and impact. Residential electricity demand is found to be price- and income- inelastic and tiered pricing alone may not be as effective in electricity conservation. The statistically significant relationship between China Energy Label efficient refrigerators - but not televisions - and lowered residential electricity consumption reflect mixed program effectiveness. Lastly, of the information feedback currently available through electricity bills, payment frequency and meters, only meter reader is estimated to be statistically significant. Important policy implications and recommendations for improving each of these three DSM measures to expand their impact on reducing residential electricity consumption are identified.     

Price and expenditure elasticities of residential energy demand during urbanization: An empirical analysis based on the household-level survey data in China

Urbanization, one of the most obvious characteristics of economic growth in China, has an apparent “lock-in effect” on residential energy consumption pattern. It is expected that residential sector would become a major force that drives China's energy consumption after urbanization process. We estimate price and expenditure elasticities of residential energy demand using data from China's Residential Energy Consumption Survey (CRECS) that covers households at different income levels and from different regional and social groups. Empirical results from the Almost Ideal Demand System model are in accordance with the basic expectations: the demands for electricity, natural gas and transport fuels are inelastic in the residential sector due to the unreasonable pricing mechanism. We further investigate the sensitivities of different income groups to prices of the three types of energy. Policy simulations indicate that rationalizing energy pricing mechanism is an important guarantee for energy sustainable development during urbanization. Finally, we put forward suggestions on energy pricing reform in the residential sector based on characteristics of China's undergoing urbanization process and the current energy consumption situations.     

Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour

Smart-metering allows electricity utilities to provide consumers with better information on their energy usage and to apply time-of-use pricing. These measures have been shown to reduce electricity consumption and induce time-shifting of demand. Less is known about how they affect residential energy efficiency investment behaviour. We use data from a randomised-controlled trial on a sample of almost 2500 Irish consumers, conducted over a 12-month period to investigate the effect of smart-metering and residential feedback on household investment behaviour. The results show that exposure to time-of-use pricing and information stimuli, while reducing overall and peak usage, can also have the unintended effect of reducing investment in energy efficiency measures within the home. Our findings indicate that households exposed to treatment were less likely to adopt any energy saving measure (23–28 % on average), and those households adopted less energy saving features than those in the control group (15–21 % on average). This result highlights the potential for behavioural interventions to have unintended consequences on behaviours other than those specifically targeted. Furthermore, it underlines the importance of examining a wider range of outcomes and allowing longer time-scales when evaluating this type of experiment.     

基于分布式算法的实时电价策略研究

在智能电网中,实时电价(RTP)是解决智能电网供需平衡的理想手段。通过分析国内外实时电价机制发展现状,将家庭用户负荷分为四类,综合考虑用户间的不同用电特性,构建了相应的用电效益优化模型,采用分布式算法,结合某地区的具体数据,并针对不同的需求响应方案、蓄电池成本、系统大小对模型进行仿真。结果表明,基于分布式算法的需求响应实时电价策略可使社会用电效益最大化。     

Demand response with locational dynamic pricing to support the integration of renewables

Electricity production from centralised and decentralised renewable energy resources in Europe is gaining significance, resulting in operational challenges in the electricity system. Although these challenges add to the locational and time dependency of the underlying cost of operating the system, this variability in time and location is not reflected in residential tariff schemes. Consequently, residential users are not incentivised to react to varying system conditions and to help the integration of renewable energy resources. Therefore, this paper provides a theoretical framework for designing a locational dynamic pricing scheme. This can be used to assess existing tariff structures for consumption and injection, and can serve as a theoretical background for developing new tariff schemes. Starting from the underlying costs, this paper shows that the potential for locational dynamic pricing depends on the locational and time dependency of its cost drivers. When converting costs into tariffs, the tariff design should be determined. This includes the advance notice of sending tariffs to users, and the length of price blocks and price patterns. This tariff design should find a balance between tariff principles related to costs, practicality and social acceptability on the one hand, and the resulting demand response incentive on the other.     

Dynamic electricity pricing—Which programs do consumers prefer?

Dynamic pricing is being discussed as one method of demand side management (DSM) which could be crucial for integrating more renewable energy sources into the electricity system. At the same time, there have been very few analyses of consumer preferences in this regard: Which type of pricing program are consumers most likely to choose and why? This paper sheds some light on these issues based on two empirical studies from Germany: (1) A questionnaire study including a conjoint analysis-design and (2) A field experiment with test-residents of a smart home laboratory. The results show that consumers are open to dynamic pricing, but prefer simple programs to complex and highly dynamic ones; smart home technologies including demand automation are seen as a prerequisite for DSM. The study provides some indications that consumers might be more willing to accept more dynamic pricing programs if they have the chance to experience in practice how these can be managed in everyday life. At the same time, the individual and societal advantages of such programs are not obvious to consumers. For this reason, any market roll-out will need to be accompanied by convincing communication and information campaigns to ensure that these advantages are perceived.     

An analysis of a demand charge electricity grid tariff in the residential sector

This paper analyzes the demand response from residential electricity consumers to a demand charge grid tariff. The tariff charges the maximum hourly peak consumption in each of the winter months Dec, Jan, and Feb, thus giving incentives to reduce peak consumption. We use hourly electricity consumption data from 443 households, as well as data on their grid and power prices, the local temperature, wind speed, and hours of daylight. The panel data set is analyzed with a fixed effects regression model. The estimates indicate average demand reductions up to 0.37 kWh/h per household in response to the tariff. This is on average a 5% reduction, with a maximum reduction of 12% in hour 8 in Dec. The consumers did not receive any information on their continuous consumption or any reminders when the tariff was in effect. It is likely that the consumption reductions would have been even higher with more information to the consumers.     

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.     

Has electric utility restructuring led to lower electricity prices for residential consumers in Texas?

This paper analyzes the determination of residential electricity prices in the competitive Electric Reliability Council of Texas (ERCOT) market. This analysis suggests that electricity restructuring in Texas has not yet resulted in lower prices for the majority of residential energy consumers in areas open to competition. Contrary to common expectations, residential electricity costs for consumers at a typical (1000 kWh per month) consumption level have increased at a greater rate in the areas of Texas offering retail choice than in the areas of the State where retail competition has not been introduced.     

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。     

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.     

Decision-making in electrical appliance use in the home

This paper presents the results of a survey as well as an argument from the viewpoint of behavioral economics with the aim of clarifying how consumers make decisions about electrical appliance use in the home. A survey of consumers showed that most have little awareness of the energy efficiency of appliances, the price of the services produced by electrical appliances, or electricity rates. These findings indicate that price does not function as a signal in electricity consumption through electrical appliance use. Rather, we found that consumer decision-making in electricity consumption is dependent on the characteristics of the particular electrical appliances they use. Additionally, we argue that the payment system for home electricity consumption plays an important role in decision-making, causing biases due to aspects of human psychology discussed here in terms of satisficing and heuristics, payment decoupling, and budgeting. We conclude that decision-making about electrical appliance use and electricity consumption in the home is not always rational and is affected both by the particular characteristics of appliances and the payment system for electricity consumption along with human psychology.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

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.     

The role of knowledge about user behaviour in demand response management of domestic hot water usage

Load balancing is an important topic in smart grid systems. Dynamic pricing is a common approach to achieve a better balance between renewable energy production and energy usage. This assumes that individual households adapt their energy usage patterns based on energy prices. However, the actual behaviour of consumers in a household is an uncertain factor that might influence the effectiveness of pricing strategies. In this paper, we investigate to what extent knowledge about actual user behaviour can contribute to local optimization of energy usage. We use simulations to study whether a smart heating system that applies a pre-heating strategy for domestic water during periods of low prices can benefit from good predictions of the user behaviour, in financial terms or in terms of energy saving. Also, we use the simulations to investigate the effect of different goal temperatures for the pre-heating strategy. The results show that pre-heating does not make a difference with respect to the energy efficiency, but that during cold months, pre-heating can result in a financial benefit. In addition, we calculate what certainty about the user behaviour is needed to be able to effectively use pre-heating during the warmer summer month. These results can help to design residential energy optimization systems.