Electricity Price Policy Research Articles

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

Exergy Analysis of Phase-Change Heat-Storage Coupled Solar Heat Pump Heating System.

With the rapid development of industrialization, the excessive use of fossil fuels has caused problems such as increased greenhouse gas emissions and energy shortages. The development and use of renewable energy has attracted increased attention. In recent years, solar heat pump heating technology that uses clean solar energy combined with high-efficiency heat pump units is the development direction of clean heating in winter in northern regions. However, the use of solar energy is intermittent and unstable. The low-valley electricity policy is a night-time electricity price policy. Heat pump heating has problems such as frosting and low efficiencies in cold northern regions. To solve these problems, an exergy analysis model of each component of a phase-change heat-storage coupled solar heat pump heating system was established. Exergy analysis was performed on each component of the system to determine the direction of optimization and improvement of the phase-change heat-storage coupled solar heat pump heating system. The results showed that optimizing the heating-end heat exchanger of the system can reduce the exergy loss of the system. When the phase-change heat-storage tank meets the heating demand, its volume should be reduced to lower the exergy loss of the tank heat dissipation. Air-type solar collectors can increase the income exergies of solar collectors.

IMPROVE THE INCREMENTAL BLOCK TARIFFS OF ELECTRICITY: TO HARMONIZE PRICING POLICY TARGETS IN THE NEW CONTEXT OF POWER SUPPLY AND DEMAND IN VIETNAM

The existing retail electricity tariff for households in Vietnam is the incremental block structure. However, the most recent revision of the electricity tariff structure was in 2014 which it was 7 years - a long period of change in conditions of electricity supply and consumption leading to the need to adjust this structural price appropriately. This article provides an in-depth analysis of the current residential block tariff of residential electricity use to identify shortcomings, thereby, for building options to improve this incremental block tariff structure to ensure the harmonization of different pricing targets especially the equity, efficiency and promote the electricity saving, in the new context of electricity supply and demand in Vietnam.Keywords: Electricity pricing policy, incremental block tariff, Electricity of VietnamJEL Classifications : D4, Q31, Q41, Q43. Q48DOI: https://doi.org/10.32479/ijeep.11534

The Critical Technical Research on the Intelligent Control Management System of the Energy Station

According to the currently equipment characteristics and process requirements of the refrigeration system of the energy station, we implement intelligent control energy-saving renovation project, which realized the automatic and intelligent control and operation of the refrigeration system related equipment. The system uses distributed control system, and provides systematic solutions for scientific decision-making, reasonable dispatch, energy management, energy efficiency analysis and energy-saving control. Simultaneously, to improve energy efficiency, this system can make full use of the electricity price policy, maximize the economic benefits of water storage, and operate in the lowest cost mode of energy supply.

Elasticities of residential electricity demand in China under increasing-block pricing constraint: New estimation using household survey data

China has implemented the residential increasing-block electricity pricing (IBEP) policy since the second half of 2012, which is considered the most effective economic instrument in improving residential energy efficiency. Price and income elasticity are two fundamental parameters to guide both scholars and policy-makers in assessing whether and to what extend Chinese households respond to the policy. However, it presents the challenge of simultaneous determination of marginal price and electricity consumption; further, it is less examined from an empirical perspective due to the absence of micro-level data. To fill this gap, this study estimates price and income elasticity by establishing two instrumental variables, based on a unique dataset from the Chinese Residential Energy Consumption Survey 2014. Results show that the residential demand for electricity is price inelastic and that electricity is an essential commodity for households in the short run. It also shows great urban-rural disparity and regional heterogeneity of household electricity consumption behavior regarding short-run income elasticity. The estimated parameters of short-run price and income elasticities provide a valuable reference for policy-making regarding both a nationwide uniform and a differential regional perspective.

Price and income elasticities of residential electricity demand in Brazil and policy implications

The research objective was to estimate Brazil's residential electricity consumption behavior, given alterations in price and income. A balanced panel with monthly data was used for the 27 states of the country between 2004 and 2019, distributed into the five regions of Brazil. This approach enabled the calculation of price elasticity and income parameters, in the short and long terms, for both national and regional scenarios. They were used, as control variables, the average temperature, which was not used in previous research due to the use of a database with a higher level of aggregation, and the rainfall, that can be considered an important parameter in the Brazilian case where most of the electricity is obtained from hydroelectric generation, however, it has never been used in previous studies. The econometric model was estimated using the Generalized Method of Moments (GMM), version System - GMM (SY - GMM), to avoid dynamic bias of the panel and a serial correlation problem, which has not been considered in previous studies in Brazil. The results show that, although the short and long-term elasticities for the national scenario are similar to previous studies when analyzed regionally, the parameters proved to be quite different from previous research. Based on the results, adjustments were proposed for the Brazilian electricity price policy.

Impact of electricity pricing strategy on residential energy consumption decisions in China: empirical evidence from a household survey

ABSTRACT In this study, the direct impact of knowledge about the electricity pricing policies on residents’ electricity saving behaviour and the indirect impact of these pricing strategies on residential consumers’ fuel choices are analysed by adopting a traditional two-stage-least-squares model. Moreover, due to the different levels of affordability of energy, differences between rural and urban households are also examined. Estimations are based on an analysis of data from 3653 households from the Chinese General Social Survey of 2015. We find that energy sources with higher health risks – raw coal and briquettes – are more likely to be used in rural households than in urban households. However, our results confirm that applying for time-of-use (TOU) tariffs can help rural residents reduce their electricity expenditure, and thus increase their ability to consume cleaner energy – LPG instead of coal. Similarly, choosing TOU and possessing a higher understanding of tiered electricity pricing (TEP) significantly improves urban residents’ electricity saving behaviours. Urban residents with higher energy saving awareness are more likely to purchase energy sources with more convenience and less carbon emissions – natural gas instead of LPG. Finally, we suggest that more information on the TEP pricing scheme should be offered to rural residents to improve their energy saving knowledge levels.

Renewable electricity generation in the Australian water industry: Lessons learned and challenges for the future

Concomitant with decarbonising the global economy, the water industry is increasingly subject to ambitious renewable electricity targets. This will be a significant transition, requiring major investments in new technology, new business models, and unprecedented rates of technology rollout. In this work, we seek to elucidate the key barriers and enablers required to facilitate a rapid switch to a future water sector comprising high levels of renewable electricity generation. Specifically, this research investigates i) the evolution of renewable electricity generation in Australian water utilities since 1998, ii) the effect of the grid electricity price and national climate change policies, and iii) the challenges that utilities are facing in achieving renewable energy targets.The study shows that the water industry has increased its on-site renewable electricity generation capacity from 26 MW in 1998 to 90 MW in 2018. Until 2010, the industry focused on recovering the embedded energy in water and wastewater as hydropower and biogas. After exploiting the economically viable embedded energy, the industry subsequently turned to solar PV as an additional source of renewable electricity. Consequently, solar PV has been the main new source of renewable electricity since 2013. The main drivers for this shift were a solar PV-centred feed-in tariff, increases in grid-sourced electricity prices, and falling technology costs. In regard to renewable electricity targets, if the industry relies solely on solar PV to achieve target electricity generation, utilities will have to install 182 MW of solar PV systems across the whole Australian industry to achieve the set targets in time.

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

Nonlinear Effect Analysis of Electricity Price on Household Electricity Consumption

The household energy consumption has been a hot field in the study of household energy consumption in recent years. With the increase of residents’ income level and the pushing of urbanization, there is a complex nonlinear relationship between energy price and energy consumption. The purpose of this paper is to investigate the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. To this direction, based on the regional characteristics of economic development in China, with the residents’ disposable income and the urbanization level as the conversion variables and the electricity sales price as the core explanatory variable, the panel smooth transition regression (PSTR) model of electricity sales price and urban household electricity consumption from the perspectives of income level and urbanization has been constructed in this paper. The empirical results show the following: (1) Under the consideration of regional difference of residents’ income level, with the increase of residents’ disposable income level, there is a significant negative correlation between electricity sales price and urban household electricity consumption in the whole country, the eastern region, and the central region, while such correlation is significantly positive in the western region. (2) Under the consideration of the difference of urbanization development level, the national regional electricity sales price and the urbanization level are positively related to the urban household electricity consumption, and the urbanization level in the western region plays the biggest role in promoting the urban household electricity consumption, followed by the eastern region and then the central region which plays the smallest role. This paper discusses the effect of electricity sales price on urban household electricity consumption from the perspective of regional difference in income and urbanization, which provides the decision-making basis and empirical support for developing regional electricity price policy and household energy consumption policy.

Understanding household electricity-saving behavior: Exploring the effects of perception and cognition factors

Based on a model that generates the theory of planned behavior (TPB) and the norm activation model (NAM), this study aimed to determine how positive and negative perceptions affect people's willingness to save electricity. It also verified the moderating effect of cognition of price and subsidy policies on residents’ electricity-saving intentions and behaviors. An empirical test was conducted using data on 658 urban residents in the Yangtze River Delta. The results indicated that perceived monetary benefit and perceived environmental benefit both positively affect residents’ willingness to save electricity, but that the effect of perceived inconvenience is significantly negative. Residents’ cognition of electricity price and subsidy policies exerts a prominent positive moderating effect between electricity-saving intentions and behaviors. In addition, perceived behavioral control and personal moral norms indirectly affect electricity-saving behaviors by affecting intentions. Finally, relevant policy recommendations for promoting energy conservation and emissions reduction in households are proposed.

An Integer Programming Model for Flow Shop Scheduling Under TOU and Tiered Electricity Price

Reasonable scheduling can achieve energy saving in the production process. Considering the TOU and tiered electricity price policy, an integer linear programming model for flow shop with the goal of minimizing the power cost is constructed, and a particle swarm optimization algorithm (PSO) with a three-layer coding method based on the workpiece, start time and machining state and the iterative process is designed. Finally, the correctness of the mathematical model and the effectiveness of the algorithm are verified by an example.

Unrelated Parallel Machines Scheduling with Sequence-Dependent Setup Times to Minimize Makespan and Tariff Charged Energy Consumption

An appropriate trade-off between total electricity costs and makespan can lead to good production planning and reduce unnecessary energy consumption. Time-of-use (TOU) electricity pricing policy has been executed in many countries which enabled industrial consumers with high energy consumption to reduce their energy costs. In this study, an unrelated parallel machines scheduling problem is considered for minimizing makespan and also energy consumption costs. Due to the importance of sequence-dependent setup times in production environments, they are considered according to the restricted duration of time periods under TOU policy. These considerations are added to the current literature. A mixed-integer bi-objective mathematical model is presented and the e-constraint method is applied to solve small and also medium-sized instances. Because the problem is shown to be NP-hard, several large-sized instances are approximately solved using Multiple Objective Particle Swarm Optimization algorithm, and Multiple Objective Simulated Annealing algorithm. Computational experiments are conducted on randomly generated data. The results show the efficiency and appropriate performance of the proposed methods.

The economic impacts of China's differential electricity pricing policy: Evidence from energy-intensive firms in Hunan Province

Differential Electricity Pricing (DEP), an energy-saving policy promulgated by China's central government for energy-intensive industries, has made great achievement in energy conservation and emission reduction. We investigate the economic effects of DEP policy using the Difference-in-Differences approach and Discrete-Time Hazard model. Based on the micro data of manufacturing firms in Hunan Province, our research found that the DEP policy has led to a decline in their output, revenue and profits. Further research found that the DEP policy has led to a decrease in employment, but hardly affected fixed assets and the probability of exiting the market. After adopting DEP policy, firms accelerate equipment upgrades but create more excess production capacity. We provide reference for a comprehensive evaluation of DEP policy.

Has increasing block pricing policy been perceived in China? Evidence from residential electricity use

Consumers' policy cognition is often ignored in evaluating policy effects. Taking the increasing block electricity pricing (IBP) system implemented in China as an example, and based on 2917 micro-survey data of residents from four first-tier cities, this paper uses the endogenous switching regression (ESR) model to investigate the impact of residents' policy cognition on electricity consumption. The results show that the treatment effect of the treatment group is −0.564, the treatment effect of the control group is −0.529. The average treatment effect is −0.549, which means that policy cognition can reduce electricity consumption by about 42.25%. The mechanism analysis results show that price cognitive bias is a crucial way for policy cognition to affect electricity consumption. Further analysis of heterogeneity shows that this energy-saving effect is larger among residents with low incomes, low electricity consumption, and residents over 50 years old. These results suggest that the government should increase the publicity of the block electricity price policy and strengthen energy conservation awareness education for price-insensitive residents to take the initiative and continue to save electricity.

Extended Horizon ECMS Control of PHEVs With 2D Electricity Price Adaptation Policy

This article proposes new improvements to the ECMS-based control of plug-in hybrid electric vehicles (PHEVs) using macro-level trip forecast information. First, the dynamic programming (DP) optimization is applied to a power-split PHEV model to obtain the optimal powertrain trajectories for benchmarking the energy economy of the proposed controllers. An instantaneous energy cost minimization process is then formulated to compute the optimal mix of fuel and electricity, subject to the state and input constraints. To improve the speed and accuracy of the energy management system, the Nelder-Mead simplex search algorithm is used for the instantaneous optimizations. To account for the battery capacity limitation, an adaptive electricity pricing policy is introduced to control battery discharge rate based on the remaining trip distance, average speed, and elevation forecasts. The parameters of the proposed policy are optimized for a group of 5 urban and highway drive cycles to obtain a robust controller which can serve a wide spectrum of driving patterns. To further improve the energy economy, an extended-horizon optimization algorithm is proposed to account for control input transitions between consecutive time steps. Finally, a revised speed forecast formula is implemented during the low-SOC intervals to lift battery SOC ahead of high-demand periods. The combination of the proposed schemes enables reaching over 99% average optimality compared to a finely discretized DP optimization for the adopted drive cycles, and above 98% optimality for a test PHEV drive cycle.

Impacts of China's differential electricity pricing on the productivity of energy-intensive industries

Using China's Industrial Enterprise Database (CIED) from 1998 to 2007, this paper investigates how the differential electricity pricing (DEP) policy affects the total factor productivity (TFP) of energy-intensive industries in China. Based on the TFP of energy-intensive industries estimated through the Olley-Pakes (OP) and Levinston-Petrin (LP) approaches, this paper estimates the treatment effects of the DEP policy through the method of propensity score matching and difference in differences (PSM-DID). The results indicate a significant negative effect of the DEP on the TFP of energy-intensive industries shortly after the DEP implementation. This finding could be explained by the rapidly increasing costs for energy-saving technologies that occurred within firms over a short time. Moreover, the estimated dynamic marginal effects demonstrate that the policy effect became positive in the following years, implying that the role the DEP policy plays shifted from increasing costs to stimulating technology improvement for energy-intensive industries. Heterogeneity analysis suggests that the treatment effects remain large and statistically significant for regions less dependent on energy-intensive industries, regions with better policy enforcement, and small-sized firms. The results remain robust in the counterfactual test, in the robustness check that takes account of the lagging effect of policy and in the robustness checks that considers more control variables or extends the time period of analysis. Based on these findings, this paper provides important policy implications for better design of the mechanisms to improve the productivity in China.

The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Policy and electricity price uncertainty provide disincentives to investors considering renewable energy investments. While electricity price uncertainty impacts on investment decisions relating to any energy investment, whether renewable or non-renewable, policy uncertainty will affect renewable energy investment decisions to a far greater extent. In this study, we consider the two main sources of uncertainty a solar Photovoltaic (PV) project is exposed to: electricity price uncertainty and policy uncertainty. We focus our analysis on utility-scale solar photovoltaics in the Pennsylvania, Jersey, Maryland Power Pool (PJM) electricity market and the New Jersey Solar Renewable Energy Credit (SREC) market. Using Solar Renewable Energy Credits as a proxy for policy, we find that there is considerable volatility in both electricity prices and policy. In a sample covering eleven years, we implement univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) and combinations of GARCH models with different weighting schemes and find that combination models provide superior forecasts. In renewable energy markets, policy supports have a significant impact on an investment’s profitability. The implication for policymakers is clear: to foster investment in solar PV, policy stability is critical.

Feasibility of pumped storage hydropower with existing pricing policy in Turkey

Pumped-storage hydroelectricity (PSH) has been used worldwide as a means of energy storage for many years. Unlike many countries with pumped storage, Turkey has not needed a PSH facility until very recently since the existing hydropower plants with large reservoirs provided the required flexibility to meet daily demand variations. The share of renewable energy in Turkey's electrical grid has significantly increased in the last decade. Moreover, the first nuclear power plant of the country is planned to start operating in 2023, reducing the hydropower share. All of these combined have begun to bring forth the need for energy storage and PSH. On the other hand, the high investment costs of PSH and present electricity pricing policy raises questions about the profitability of a projected PSH facility. In this paper, the Gökçekaya PSH project is selected as a case study to determine the profitability of a PSH implementation in Turkey considering probable operation scenarios based on hourly prices without any reference to environmental issues to highlight the consequences of pricing policy on storage schemes. Evaluations are made using real-time electricity prices and generation-consumption values through the perspective of both the public and private sectors. Results show that the current prices in the Turkish electricity market are not profitable enough for potential PSH projects to attract investment by the private sector.

Prefeasibility study of a distributed photovoltaic system with pumped hydro storage for residential buildings

Distributed renewable energy systems are vital for satisfying the increasing demand for electricity in cities due to an influx of people. Nowadays, the residential buildings in metropolitan cities like Shanghai have natural height differences for potential energy, this advantage has not yet been fully exploited currently. To better utilize renewable energy, a grid-connected photovoltaic with pumped hydro storage system is first proposed for residential buildings, the operation principle of this system is then developed. Under Shanghai's time-of-use electricity price policy, the proposed methodology is applied in a case study to investigate the techno-economic feasibility of its application on villa and apartment residential buildings. Moreover, some technical indicators, such as self-consumption rate and self-sufficiency rate, are employed to evaluate the performance of the proposed methodology under different scenarios. The results demonstrate that the pumped hydro storage system on the apartment buildings can absorb more surplus power (4.83 kW h/d) from the photovoltaic system than on the villa buildings, meanwhile, it can also release more power (3.69 kW h/d) to meet the load demand. For residents of villa buildings and apartment buildings, the initial total investment is 35,417 and 36,423 Chinese yuan, respectively, while the payback periods are 9.01 and 7.06 years respectively. Moreover, a sensitivity analysis is conducted for the apartment buildings, revealing that the photovoltaic capacity and load demand are the two key parameters affecting the cost and revenue of trading with the grid. A 25% increase in photovoltaic capacity can produce 123.3% higher net revenue value than the base case, and a 25% increase in load demand leads to a 102.7% decrease in net revenue. The variations in building height and upper reservoir volume have a marginal impact on net revenue compared with photovoltaic capacity and load demand.