Electricity Price Changes Research Articles

Influencing factors on risk perception of IoT-based home energy management services

This study examines factors influencing the risk perception of Internet of Things (IoT) services in the home energy management sector. We focus on investigating the effects of the various types of perceived risks and individuals’ propensities on the overall risk perception of the IoT services and explore some demographic variables. The perceived risks include perceived financial risk, perceived performance risk, perceived security/privacy risk, and perceived electromagnetic radiation (EMR) risk. The individuals’ propensities include the sensitivity to electricity price changes, environmental destruction, and new technology acceptance. As a result, this study confirms the existence of various influencing factors in the risk perception of the IoT-based home energy management services. Perceived EMR risk is the most influential one among the various types of perceived risks of the services, followed by perceived performance risk and perceived security/privacy risk. In general, experts point out cybersecurity threats as a weakness of ICT services, but users are more likely to have fear of the EMR. Moreover, sensitivity to electricity price changes and sensitivity to new technology acceptance are significant factors. This study has a significance in that we explore various factors affecting the risk perception of IoT services in the energy management sector in detail and comprehensively. Particularly, we discuss not only perceptions about the IoT services, but also individuals’ propensities. This work may contribute not only to studies on perceived risk and technology acceptance but also to stakeholders trying to market the IoT services to consumers.

A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System.

Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.

Commercial feasibility of an integrated closed-loop ethanol-feedlot-biodigester system based on triticale feedstock in Canadian Prairies

This paper presents techno-economic assessment of a closed-loop integrated system combining ethanol plant, feedlot, and biodigester in the Canadian Prairies. Triticale is the primary feedstock for ethanol production. Wet distiller's grains (WDG) and thin stillage from the ethanol plant is used as feed for feedlot beef cattle. Feedlot manure is used to produce methane via anaerobic digestion (AD), for subsequent conversion to electricity and heat through a combined heat and power generation facility. Three scenarios and two system scales were investigated. Total investment in the integrated system was $38–54 (small scale) and $132–237 million (large scale). The results showed that only one scenario in a large scale case has potential to generate profit; in this case, only the feedlot generated positive net present value (NPV) due to savings in feed cost of feeding WDG. Both the ethanol plant and biodigester generated negative returns under the integrated system. Co-locating a feedlot with an ethanol plant enhances ethanol plant profitability along with generation of extra revenue from feedlot operations. Compared to the non-integrated feedlot, the integrated feedlot saves $71.51 in feed cost per cow when cattle are fed 25% WDG. Electricity and heat from the biodigester could only supply 7–20% and 46–71% of the demand of the ethanol plant respectively. Sensitivity analysis tested key factors affecting the profitability of the subsystems. The ethanol plant generates profit under higher ethanol prices. However, NPV for all biodigesters is negative, even under favourable changes in investment and electricity prices. The study also showed that incorporating straw into ethanol fermentation and biogas production was not economically profitable in comparison with single triticale grain fermentation and manure digestion. Overall results suggest need for more policy support to improve economics of anaerobic digesters under current conditions, in the context of clean technology strategies.

Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems

The interdependency across natural gas, power and heating systems is increasingly tightened due to the wide development of cogeneration plants and electrified heating facilities. Multi-energy integration is a prevalent trend and the energy hub, which acts as an intermediary agent between providers and consumers, is expected to play a central role in allocating energy resources more efficiently. However, uncertainties originating from multiple kinds of energy demands challenge the operation of energy hubs and may compromise system efficiency. Energy trading and sharing among individual hubs offer a unique opportunity to increase system flexibility and reduce the cost under demand uncertainty. In this paper, three quintessential schemes for organizing a cluster of energy hubs at demand side, i.e., individual, sharing market, and aggregation, are studied under a stochastic framework with probabilistic load forecasts. First, we perform theoretical analysis and compare their economic efficiencies from a maximum-utility (or minimum-cost) perspective. Utility curves of respective schemes are given, and several important phenomena are revealed from the economic analysis. Then we discuss the concrete decision-making models of energy hubs under the three schemes, taking into account the change of electricity price in response to the total demand, which give rise to bilevel optimization problems and are technically transformed into mixed-integer linear programs. Finally, we conduct numerical experiments, which validate the theoretical outcomes, and reveal that the sharing scheme can achieve nearly optimal efficiency without a central organizer, and hence appears to be a promising direction for future multi-energy systems.

Socioeconomic cost-benefit-analysis of seasonal heat storages in district heating systems with industrial waste heat integration

Industrial waste heat is primarily available in summer months while district heating demand is greater in winter months. In order to shift heat potentials from summer to winter and thereby make the feed-in of industrial waste heat economically more attractive, the paper explores the use of waste heat with large-scale (seasonal) heat storage. This paper focuses on the case study of the industrial city of Linz (Austria), and demonstrates the advantages and disadvantages of seasonal heat storage. The interaction between the storage system with optimal cogeneration plant dispatch and industrial waste heat integration is explained. Furthermore, the most important parameters of the heat storage in order to achieve economic feasibility are highlighted. One main finding is that the number of annual cycles is crucial for a seasonal heat storage. The amortization period is computed to be about 20 years, and is shown to be extremely sensitive to changes in electricity, gas and CO2 prices.

A socio-technical analysis of consumer preferences about energy systems applying a simulation-based approach: A case study of the Tokyo area

In consideration of forthcoming amendments to laws and regulations about Japan’s energy systems, we conducted a socio-technical analysis to support discussions towards a consensus on socially acceptable and technologically feasible energy systems for Japan. Using a choice experiment survey, we analysed consumer preferences about the electricity supply in the Tokyo area. We found that consumers tend to purchase electricity from major providers with a renewable-energy component in the mix, which offers stable supply on cheap plans with little price volatility. Furthermore, they avoid nuclear power, and show a positive “willingness to pay” for regional power providers only after possible regional economic benefits are suggested. In our subsequent technical analysis, consumers’ estimated willingness-to-pay for the preferred nuclear-free and renewable energy options were compared with simulated electricity price changes in response to the roll-out of feasible energy systems technologies under different policy scenarios, using computational energy flow models. We found that simulated price increases from an immediate shutdown of nuclear plants and from feed-in tariffs for renewables would exceed consumers’ willingness-to-pay. Our approach of using survey- and simulation-based analyses jointly to facilitate informed discussions on energy system choices should lead to continuous improvements in public and policy discussions towards a consensus.

Study on the Matrix Model of Electricity and Electricity Price

Electric vehicles change the charging time of electric vehicles according to the change of electricity price, so that the electric vehicle can be transferred as a load. The solution of electricity price is different, and its essence is based on definition. The change of electricity price has changed the influence of consumers on electricity demand. It is necessary to study and formulate TOU price according to the actual situation. In this paper, the solution of the power tariff matrix is deeply analyzed, which provides a theoretical basis for the study of TOU price and has practical significance.

Comprehensive Comparative Analysis of Different Regional Energy Supply Schemes

A comprehensive comparative analysis of different energy supply schemes is carried out to solve the coordinated supply problem in regional multi-energy complementary systems. Firstly, two distributed energy supply schemes, respectively gas turbine CCHP system and environment-source heat pump system, are proposed, and corresponding operation strategies are formulated; next, the safety and environmental performances of the municipal centralized energy supply scheme and the above two distributed energy supply schemes are analyzed; then, a probabilistic life-cycle cost model is established, and the calculation methods for the economic efficiencies of the three energy supply schemes are presented; finally, in the case of one certain commercial region in northern China, the comprehensive evaluation on the three energy supply schemes considering economy, safety and environmental performance is carried out. Based on sensitivity analysis, the impacts of natural gas price and electricity price on the economic efficiency of different energy supply schemes are obtained, which indicate that as the natural gas price and electricity price change, the scheme with the optimal economic efficiency will change correspondingly. As a result, a rational energy supply scheme in practical projects should be formulated considering the focus of the planning area and the local energy prices.

Switching on Electricity Demand Response: Evidence for German Households

Empirical evidence on the response of German households to electricity price changes is sparse. Using panel data originating from Germany’s Residential Energy Consumption Survey (GRECS), we fill this void by employing an instrumental variable approach to cope with the endogeneity of the consumers’ tariff choice. By additionally exploiting our information on the households’ knowledge about power prices, we also employ an Endogenous Switching Regression Model to estimate price elasticities for two groups of households, finding that only those households that are informed about prices are sensitive to price changes, whereas the electricity demand of uninformed households is entirely price-inelastic.

Profit optimization of sustainable low-to-medium temperature waste heat recovering management

PurposeResponding to natural resource depletion and carbon dioxide (CO2) emission problems, and also the stricter government’s energy regulations, the purpose of this paper is to develop a sustainable waste heat recovery optimal-profit-oriented management model especially targeting on the easily forgotten low- and medium-temperature waste heat in the industry. In the paper, a system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit.Design/methodology/approachThis paper integrates an efficient Organic Rankine Cycle (ORC) system from both sustainable energy reservation and cost effectiveness approaches with an optimization model that adopts particle swarm optimization (PSO) algorithm to determine proper installation locations and feasible generator sets. The system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit. The model considers the environmental factors: temperature, heat amount, equipment configuration of the number of ORC sets, and detailed investment cost constraints.FindingsThe results show that annual investment return rate, annual increase in electricity, power generation efficiency, and annual CO2 emission reduction are all highly improved, and investment recovery period is shortened. Also, the larger scale of the waste heat emission, the better the performance is achieved. Finally, the study also completes a sensitivity test under dynamic conditions of electricity price, generator sales price and factory budget constraints, and the results are consistently robust. More valuably, this paper demonstrates applications on two different manufacturing industries with various waste heat emission scales to prove the accountability.Originality/valueThe main contributions are in three aspects. First, it proves that applying PSO to a nonlinear mathematical model can help determine the optimal number and style configuration of generators for waste heat sources. Second, different from the prior research works focusing on power generation, this paper also deliberates the cost factors, cost of generators, costs of numerous peripheral components and future maintenance costs to ensure the factories not conflict with the financial limitations. Third, it is not only successfully applied in two industries with different scales, but also robust with various economic tests, electricity price change, generator sales price change, and investment budget adjustments.

An Analysis on the Changes in Electricity Price on Industrial Production in Korean Manufacturing Industries

An Analysis on the Changes in Electricity Price on Industrial Production in Korean Manufacturing Industries

The Impact of China's Electricity Deregulation on Coal and Power Industries: Two-Stage Game Modeling Approach

The regulated price mechanism in China’s power industry has attracted much criticism because of its incapability to optimize the allocation of resources. To build an “open, orderly, competitive and complete” power market system, the Chinese government launched an unprecedented marketization reform in 2015 to deregulate the electricity price. This paper examines the impact of the electricity price deregulation in the industry level. We first construct two-stage dynamic game models by taking the coal and coal-fired power industries as the players. Using the models, we compare analytically the equilibriums with and without electricity regulation, and examine the changes in electricity price, electricity generation, coal price and coal traded quantity. The theoretical analyses show that there are three intervals of the regulated electricity sales prices which influence the impact of electricity price deregulation. Next, we collect empirical data to estimate the parameters in the game models, and simulate the influence of electricity deregulation on the two industries in terms of market outcome and industrial profitability. Our results suggest that the actual regulated electricity price falls within the medium interval of the theoretical results, which means the price deregulation will result in higher electricity sales price but lower coal price, less coal traded amount and less electricity generation amount. The robustness analysis shows that our results hold with respect to the electricity generation efficiency and price elasticity of electricity demand.

Multi-objective hydropower purchase optimization method for inter-provincial power grids under time-sharing electricity price

The existing electric grid purchase ratio research focuses on the methods of various types of combinations of purchasing and selling business of electricity, and the methods of reasonable risk allocation between different businesses, First, it lacks the consideration of the sensitivity of customer requirements to electricity price changes . Second, it lacks the consideration of optimization of the revenue and risk of hydropower purchase in the provincial electric grid. Therefore, this paper establishes the multi-objective time-of-use power price model based on customer requirements for time-of-use power price response.It introduces a VaR-based risk assessment method. Also, it proposes a multi-objective optimization model that maximizes the expected revenue on electricity purchase and minimizes the risk of purchasing electricity. The electricity purchase ratio scheme and the electrictiy purchase risk scheme are jointly optimized to obtain the electric grid inter-provincial electrictiy purchase risk decision and the optimal electricity purchase ratio. The results show that the grid company will obtain greater economic benefits while avoiding risks as much as possible after using the power purchase optimization method of this paper.

Social cost of carbon pricing of power sector CO2: accounting for leakage and other social implications from subnational policies

In environments where climate policy has partial coverage or unequal participation, carbon dioxide (CO2) emissions or economic activity may shift to locations and sectors where emissions are unregulated. This is referred to as leakage. Leakage can offset or augment emissions reductions associated with a policy, which has important environmental and economic implications. Although leakage has been studied at national levels, analysis of leakage for subnational policies is limited. This is despite greater market integration and many existing state and regional environmental regulations in the US. This study explores leakage potential, net emissions changes, and other social implications in the US energy system with regionally differentiated pricing of power sector CO2 emissions. We undertake an economic analysis using EPRI’s US-REGEN model, where power sector CO2 emissions are priced in individual US regions with a range of social cost of carbon (SCC) values. SCC estimates are being considered by policy-makers for valuing potential societal damages from CO2 emissions. In this study, we evaluate the emissions implications within the SCC pricing region, within the power sector outside the SCC region, and outside the power sector (i.e. in the rest of the energy system). Results indicate that CO2 leakage is possible within and outside the electric sector, ranging from negative 70% to over 80% in our scenarios, with primarily positive leakage outcomes. Typically ignored in policy analysis, leakage would affect CO2 reduction benefits. We also observe other potential societal effects within and across regions, such as higher electricity prices, changes in power sector investments, and overall consumption losses. Efforts to reduce leakage, such as constraining power imports into the SCC pricing region likely reduce leakage, but could also result in lower net emissions reductions, as well as larger price increases. Thus, it is important to look beyond leakage and consider a broader set of environmental and economic metrics. Leakage rates, net emissions outcomes, electricity price changes, fuel market effects, and macroeconomic costs vary by region of the country, time, policy stringency, policy design (e.g. leakage mitigation provisions), policy environment in neighboring regions, and price responsiveness of demand.

Electricity residential demand elasticities: Urban versus rural areas in Portugal

We study the difference between urban and rural elasticities for electricity residential consumption in Portugal. Our sample covers the period from 1989 to 2010 and consists of five independent surveys. We adopt the pseudo-panel methodology creating 350 cohorts based on the size of the household, the type of location (rural or urban), the region, and the income quintile. Our results indicate that rural and urban populations have different behaviors. In particular, rural households are more sensitive to price changes than urban ones. This may happen because rural populations can more easily use other energy sources, for example, wood, for cooking and heating. As a consequence urban populations will be more affected by electricity price changes.

Effect of Utility Deregulation and Mergers on Consumer Welfare

<p>In the late 1990s, many U.S. states deregulated electric utilities, allowing for competition among power generators. Deregulated states then adopted retail choice programs, allowing customers to choose their power provider. In addition, a significant merger wave among large utilities ensued. How did these events impact consumer welfare? This study examines the effects of utility deregulation and mergers, by analyzing electricity price and output changes among deregulated and regulated states. I find that deregulation may have had a positive effect when states adopted certain measures, such as retail choice or fuel changes, that enhanced competition and lowered costs. Mergers also affected consumer welfare, with differential impacts found between the merger of generation firms versus the merger of generation and transmission companies. </p>

Effect of Utility Deregulation and Mergers on Consumer Welfare

<p>In the late 1990s, many U.S. states deregulated electric utilities, allowing for competition among power generators. Deregulated states then adopted retail choice programs, allowing customers to choose their power provider. In addition, a significant merger wave among large utilities ensued. How did these events impact consumer welfare? This study examines the effects of utility deregulation and mergers, by analyzing electricity price and output changes among deregulated and regulated states. I find that deregulation may have had a positive effect when states adopted certain measures, such as retail choice or fuel changes, that enhanced competition and lowered costs. Mergers also affected consumer welfare, with differential impacts found between the merger of generation firms versus the merger of generation and transmission companies. </p>

Response to Extreme Energy Price Changes: Evidence from Ukraine

Large but temporary price increases are sometimes deployed on days when the demand for electricity is extremely high due to exceptionally warm or cold weather. But what happens when the extreme price changes are permanent? Between January 2013 and April 2016, natural gas and electricity prices in Ukraine increased dramatically (up to 300% of the initial rates). We exploit variation in tariffs over time and across customers to estimate the price elasticity of electricity demand using a panel dataset with monthly meter readings from households in the city of Uzhhorod in Ukraine. We ask three research questions. First, what is the price elasticity of consumption implicit in the response (if any) to these large electricity price changes? Second, is there evidence of heterogeneity in the price elasticity of electricity demand driven by dwelling or household characteristics, or by consumer understanding of block pricing and/or own consumption levels? Third, how quickly do household adjust their consumption after a price change? Histograms of the monthly usage records suggest that our Ukrainian consumers were aware of the increasing block pricing system and responded to marginal prices, with bunching observed at the then-current as well as future block cutoffs. The price elasticity of electricity demand is approximately -0.2 to -0.5, with the bulk of our estimates around -0.3. The elasticity becomes up to 50% more pronounced over the first three months since prices change. We find only limited evidence that persons who are attentive about their consumption levels, their bills, or the tariffs are more responsive to the price changes. The tariff increases do help reduce CO2 emissions, but at a high cost per ton.

System Dynamics Modeling of Households' Electricity Consumption and Cost-Income Ratio: a Case Study of Latvia

Abstract Increased energy efficiency of the building sector is high on the list of priorities for energy policy since better energy efficiency would help to reduce impact on climate change and increase security of energy supply. One aim of the present study was to find a relative effect of growth of demand for energy services due to changes in income, energy consumption per unit of demand due to technological development, changes in electricity price and household income on household electricity consumption in Latvia. The method applied included system dynamics modeling and data from a household survey regarding the relationship between electricity saving activities and the electricity cost-income ratio. The results revealed that, in direct contrast to the expected, a potential reduction of the electricity consumption is rather insensitive to electricity price and electricity cost-income ratio, and that the efficiency of technologies could be the main drivers for future electricity savings. The results suggest that support to advancement of technologies and faster replacement of inefficient ones rather than influencing the energy price could be effective energy policy measures. The model, developed in the study could be used in similar assessments in other countries.

Employment impacts of renewable energy policies in China: A decomposition analysis based on a CGE modeling framework

Employment impacts are one of the most important social impacts associated with the development of renewable energy, and are also one of the key concerns for policy makers designing renewable energy policies. Current studies tend to focus on the direct job changes in renewable sectors per se and on the indirect job changes along value chains of renewable energy, therefore depicting a picture of prosperity with large amounts of “green jobs”. However, the induced job changes in other sectors that are not directly in the value chains but are still influenced by electricity price changes and related financial resource transfers have usually been neglected, resulting in an incomplete and potentially biased understanding of this specific category of social impact. By using a computable general equilibrium (CGE) model of China that incorporates detailed renewable power generation technologies and considers labor market imperfections, our study tries to fill this gap and quantifies the full scope of job changes (direct, indirect and induced) brought by renewable energy development in China. Results show that per 1 TW h expansion of solar PV and wind power would create up to 45.1 thousand and 15.8 thousand, respectively, direct and indirect jobs in China. However, the scale of induced job changes is quite significant and may even lead to net job losses in the whole economy in some cases. We have further revealed the sectoral contributors to total job changes. In all, there are no assured conclusions on the occurrence of green jobs when developing renewable energy. The impacts are highly dependent on the species of renewable energy, the financing mechanisms for renewable subsidies, and the scopes of employment impacts. We suggest that full-scope employment impacts should be carefully considered and the detailed supporting policies should be carefully designed by decision makers when promoting renewable energy.