Price Signals Research Articles

RETRACTED:Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network.

Valuing the social cost of carbon: Do economists really care about climate change?

The aim of the article is to analyse the scientific literature of the last decade on the social cost of carbon to identify and discuss the best values representing the external cost of CO2 emis-sions calculated with a damage valuation approach, to use in cost-benefit analysis and in other external costs applications, such as Life Cycle Assessment. The social cost of carbon repre-sents the present value of the global damages of climate change attributable to the emission of an additional ton of carbon dioxide into the atmosphere. The measurement of the social cost of carbon is fundamental in the transition to a net zero emissions economy because it allows bal-ancing the costs of investments needed for decarbonization with their benefits in terms of avoided climate damages. The paper develops and applies a method for identifying the best social cost of carbon estimates, that starts from study reviews conducted within institutional processes (IPCC, OECD, US Government bodies), to then analyse the study reviews made in academic or research contexts and only in the end it delves into selected academic studies that provide original estimates of the social cost of carbon. The method allowed to identify and dis-cuss a robust even if conservative estimate of the social cost of carbon for emissions in the period 2020-2080, recently elaborated by the US Environmental Protection Agency, that can be used worldwide, for example to update the reference external cost values for CO2 emis-sions recommended by the European Commission Handbook on external cost of transport.

Bidding strategy of storage hydropower plants in reserve markets

The increasing share of intermittent sources of energy will increase the need for frequency-control reserves. However, the supply from gas and coal-fired power plants might decrease in the following years. Being the procurement of reserves mostly market-based in Europe, the market design should send price signals to encourage participation in these markets. This pa-per analyses the incentives provided by the French market design for seasonal storage and pumped storage hydropower plants to participate in reserve markets. To that end, a determinis-tic mixed-integer linear optimization model is presented. The objective is to maximize profits in the energy and reserve markets according to 2019 market prices. By optimising the trade-offs between the day-ahead and the reserve markets, the storage hydropower plant increase its profits. The pumped storage hydropower plant sometimes chooses the Frequency Contain-ment Reserve market or the day-ahead market only. The apparition of some hours of FCR par-ticipation with the pumped storage plant is explained by its higher number of generating hours and by the higher volatility of reserve energy prices. These two factors also explain the greater response of the pumped storage plant to the incentive measures on the FCR market.

Field study of energy and environmental performance of ground source heat pumps retrofitted in a cluster of UK social housing dwellings

This paper uses an empirical field based approach to examine seasonal variation in heat pump electricity use and indoor temperatures across a cluster of 50 social housing dwellings retrofitted with ground source heat pumps (GSHP). The case study dwellings were co-located in a socially deprived housing estate in Oxford (UK). Time series monitoring data was collected on indoor, outdoor and target temperatures, heat pump electricity use (kWh), space heating and hot water operation status (on/off) in non-heating and heating seasons. Contextual data about the dwellings were gathered through heat pump surveys and energy performance certificates. Results showed 60% rise in electricity use during the heating season with statistically significant correlation observed between indoor and target temperatures, energy efficiency rating and monthly electricity use. When electricity use was normalised for outdoor temperature, lower electricity use per heating degree day (HDD) was observed in the heating season in the majority of dwellings, confirming the efficient running of the heat pumps. Mean daily indoor temperatures were found to be stable across majority of dwellings, with peak electricity use largely happening outside of the national evening peak period of 4pm-7pm. Despite there being a ‘Timer’ mode in relation to outside weather, most of the residents used the ‘Manual’ mode of operation to adjust target temperatures, possibly to overcome the low output temperatures of heat pumps. While resident training will help to enhance understanding of heating controls, smarter operation of heat pumps through automation based on optimisation of indoor and outdoor temperature, occupancy patterns, heating preferences and electricity price signals could result in further energy savings.

Evidence of households' demand flexibility in response to variable hourly electricity prices – Results from a comprehensive field experiment in Norway

Demand-side flexibility which reacts to hourly price signals is expected to play an essential role in balancing intermittent electricity generation in the future carbon-neutral power system. This study investigates the price responsiveness of households and various influencing factors through a pricing experiment which was implemented as a reward scheme and involved 3746 Norwegian households. These households, characterised by a highly electrified energy usage such as electric heating and charging electric cars, were subjected to variable hourly price signals with a one-day advance notification over the course of three winter months. The study reveals that households reduced their electricity demand by, on average, 2.92% in hours with high prices, and the reduction did not diminish significantly after repeated interventions. Moreover, an increased response could be observed for price signals with a short peak price period and when prices exceeded a threshold of 15 NOK/kWh. These results suggest that despite the limited potential of manual demand response from households, it can still be relied upon and utilised to enhance power system operations and planning. Additionally, to fully exploit its potential, it is recommended to enhance price responsiveness through tailored price information and by incentivising investments in automatic response and energy storage.

AI temporal planning for energy smart buildings

Buildings are responsible for about one-third of industrialised countries’ overall energy consumption and greenhouse gas emissions. As if this was not enough, recently, energy prices significantly increased and affected all economic areas. Making buildings more efficient and effective is the step needed toward cost reductions. Key enablers of cost-effectiveness are leveraging batteries, awareness of and adaptability to energy prices, and integrating powerful reasoning techniques to optimally and flexibly operate buildings. Researchers have tackled many of these aspects using a variety of approaches. Whereas a less investigated one is that of AI planning to coordinate actions and save energy in buildings. However, generating plans based on signals of energy prices and leveraging batteries is still an open research problem. To address this high-potential aspect, we engineer an AI planning system for improving the energy-cost effectiveness in buildings by coordinating the building’s operation based on day-ahead prices and the use of a battery, all without sacrificing the comfort of building occupants. We propose to exploit temporal planning due to its powerful modelling and reasoning features, especially in explicitly addressing time. We evaluate the effectiveness of the system in several scenarios with varying building environmental conditions. We compare the energy cost from using our planning system to a baseline cost, where we record a reduction of 43rage in favour of our system.

Measuring the competitiveness of commodity markets using price signals and information theory

Technological advancements, abrupt changes in market conditions, and political reforms, among other things, necessitate strong regulatory oversight, and accurate measurement of performance related indicators. The more accurate, information rich, and transparent these measurements/signals, the lower the level of uncertainty felt by value chain participants, who are thus able to recognize and observe whether the market’s state is efficient. Its lack, may lead to indecisiveness, translating into false interpretations that could lead to wrong policy directions. This paper provides an ex-post evaluation tool intending to deliver additional insights or quality information that would aid the regulator in assessing the state of the market. The tool is applied to the UK wholesale natural gas market for the period between 2011 and 2020, assessing and testing the market’s weak-form efficiency. It claims that today’s gas prices reflect a specific type of information, primarily past gas prices, and that only new information can help predict future prices. In this manuscript, based solely on a limited and available untapped dataset (day-ahead price time series), and working under the assumption that gas prices are the result of market processes, a variety of information metrics (gas price randomness, distribution of extreme prices, ability to predict prices - based on historical sets) is extracted with the use of suitable mathematical statistical models. A weighted entropy index is then computed, and measures the state of the commodity market. The results indicate that the analysis has helped gain information, thus reducing uncertainty (relative to a pre-analysis) by 86.5 %. Additionally, there is sufficient evidence that the UK natural gas prices are weak-form efficient.

Evaluating demand charges as instruments for managing peak-demand

Reducing peak demand in distribution grids is associated with benefits such as delayed infrastructural investments, decreased losses and a reduced risk of power deficits. One instrument aimed at reducing peak demand is the demand charge, a capacity-based component in a network tariff that intends to encourage users to reduce their peak usage. Using ten years of data from a Swedish distribution network, this study demonstrates that demand charges may be unsuitable for managing the problems they are intended to address. Two critical misalignments in the design of these demand charges are identified: 1) Demand charges are most commonly based on maximum billing demand – a given user's maximum monthly peak – whereas the problem of peak demand overwhelmingly concerns maximum system peaks in the distribution grid as a whole. The lack of coincidence between these peaks suggest that demand charges are, by design, ineffective for reducing peak demand. 2) The peaks which determine a distribution system's maximum capacity requirements are rare, seasonal and largely temperature-driven events, whereas demand charges mainly target users' habitual daily patterns, encouraging daily shifts from peak to off-peak hours. As long as the main driver of network costs, maximum system peaks, are absent in their design, demand charges will neither reflect the costs that users impose on the grid nor provide them with the correct price signals on how to best act.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Flood Risk Mapping and the Distributional Impacts of Climate Information

This paper examines the provision of official flood risk information in the United States and its distributional impacts on residential flood insurance take-up. Assembling new data on all flood maps produced after Hurricane Katrina, I first document that updated maps decreased the number of properties zoned in high-risk floodplains and incorrectly omitted five million properties that should have been rezoned inside these areas. Removals and omissions disproportionately occurred in neighborhoods with more Black and Hispanic residents. Leveraging the staggered timing of map updates, I estimate they decreased flood insurance take-up and exacerbated racial disparities in insurance coverage. These effects are driven by beliefs about risks and regulatory constraints. Correcting flood maps could increase welfare by $20 billion annually, but past updates decreased welfare due to distorted risk and price signals.

Hybrid wavelet/Elman NN model for short term cost prediction utilizing developed deer hunting optimizer

The cost signal of electricity in the competitive electrical energy marketplaces is of special importance for all planning and operation activities. Also, the price of electricity has an uncertain nature and various factors affect it in the short and long term. Factors active in the electricity market need to accurately and effectively forecast the electricity price signal to manage risk in the market. For estimating future electricity prices, this research suggests a combined procedure on the basis of Elman neural network model and the wavelet transform. The proposed Elman neural network/wavelet transform forecasted the next hour's power price based on the past 24 h' pricing. This research uses an optimized Elman neural network using a developed deer hunting optimizer and the total model is named Elman neural network/developed deer hunting optimization-wavelet transform. In this paper, Data of Zone Preliminary Billing is used for establishing the training of model and forecasting of performance. The method is then compared with some other published works and the outcomes demonstrate the offered approach superiority toward those for the electrical cost predicting.

For better or for worse? On the economic and ecologic value of industrial demand side management in constrained electricity grids

Electricity systems are in dire need to scale up volatile decentralized renewable electricity supply (RES), reducing the dependency on fossil fuels and drastically cutting carbon emissions. Industrial demand side management (DSM) is a key enabler of the energy transition, as it allows to align large shares of electricity consumption with (renewable) supply. We present a short-term market model to evaluate the economic value (i.e., reduced system costs) and ecologic value (i.e., reduced carbon emissions) of industrial DSM within a constrained electricity grid, incorporating conventional electricity supply (flexibility) and RES. A model evaluation yields four key findings: (1) Grid-capacity constraints determine whether and to what extent industrial DSM may contribute economic and ecologic value. (2) Both the economic and ecologic value of industrial DSM are determined by the location within a capacity-constrained electricity grid. (3) DSM might even increase carbon emissions depending on local merit-orders. (4) Carbon emission pricing may resolve a potential conflict between ecologic and economic DSM-value maximization. Based on these findings, energy policy should (i) establish an electricity market design that provides sufficient local price signals for industrial DSM and (ii) ensure that local merit-orders incentivize industrial DSM to contribute both economic and ecologic value based on appropriate carbon prices.

Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

Identifying carbon market price signals and accurately predicting carbon prices is of great significance in the development of carbon markets and carbon trading management. In this study, a fast ensemble empirical mode decomposition (FEEMD) method was used to analyze the fluctuation patterns of carbon market prices and obtain their characteristic components. Then the characteristic components of carbon prices were reconstructed using sample entropy theory (SRSE), and a partial autocorrelation analysis (PACF) technique was used to screen the data points exerting large impacts on carbon market prices. The FEEMD-SRSE-PACF-ILSTM hybrid model constructed. The main innovation points are as follows: The FEEMD is proposed to solves the common modal aliasing problem during carbon price decomposition and improves the efficiency of the carbon price sequence decomposition; the sample entropy theory and partial autocorrelation analysis methods are applied to reconstruct the characteristic components of carbon prices; the LSTM neural network model was optimized by the grey wolf algorithm to enhance its stability and adaptability. The analyzed price changes of large regional carbon markets in China, the European Union, and the United States can be effectively used to predict prices in the carbon market and guide its participants towards low-carbon investment and trading.

Measuring Total Carbon Pricing

Abstract While countries increasingly commit to pricing greenhouse gases directly through carbon taxes or emissions trading systems, indirect forms of carbon pricing—such as fuel excise taxes and fuel subsidy reforms—remain important factors affecting the mitigation incentives in an economy. Taken together, how can policy makers think about the overall price signal for carbon emissions and the incentive it creates? We develop a methodology for calculating a total carbon price applied to carbon emissions in a sector, a fuel, or the whole economy. We recognize that rarely is a single carbon price applied across an economy; many direct carbon pricing instruments target specific sectors or even fuels, much like indirect taxes on fossil fuels; and carbon and fuel taxes can be substituted for one another. Tracking progress on carbon pricing thus requires following both kinds of price interventions, their coverage, and specific exemptions. This inclusive total carbon pricing measure can facilitate progress in discussions on minimum carbon price commitments and inform assessments of the pricing of carbon embodied in traded goods. Calculations across 142 countries from 1991 to 2021 indicate that although direct carbon pricing now covers roughly one-quarter of global emissions, the global total carbon price is not that much higher than it was in 1994 when the United Nations Framework Convention on Climate Change entered into force. Indirect carbon pricing still comprises the lion's share of the global total carbon price, and it has stagnated. Taking these policy measures into account reveals that many developing countries—particularly net fuel importers—contribute substantially to global carbon pricing. Tackling fuel subsidy reform and pricing coal and natural gas emissions more fully would have a profound effect on aligning carbon prices across countries and sectors and with their climate costs.

Joint energy-frequency regulation electricity market design for the transition towards a renewable-rich power system

Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes.

Long-Run Labor Costs of Housing Booms and Busts

Abstract We show large flows of workers into the real estate agent (REA) occupation during the early 2000s from virtually all parts of the skill, wage, and education spectrums. We find those entering REA in Metropolitan Statistical Areas (MSAs) with house price bubbles end up in occupations paying significantly less in the long-run as compared to similar REA entrants in non-bubble areas. Even in 2017, when house prices and employment return to their pre-crisis levels, REA entrants in Bubble MSAs are in occupations earning about 6% less. These results point to lasting effects of labor allocation decisions in response to distorted price signals.

Identifying generalizable equilibrium pricing strategies for charging service providers in coupled power and transportation networks

Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), plays a critical role for global energy transition and decarbonization. In this context, coordination of EV routing and charging activities through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled power-transportation networks (CPTN). This work examines the non-cooperative pricing competition between self-interested EV charging service providers (CSP), taken into account the complex interactions between CSPs' pricing strategies, EV users' decisions and the operation of CPTN. The modeling of CPTN environment captures the prominent type of uncertainties stemming from the gasoline vehicle and EV origin-destination travel demands and their cost elasticity, EV initial state-of-charge and renewable energy sources (RES). An enhanced multi-agent proximal policy optimization method is developed to solve the pricing game, which incorporates an attention mechanism to selectively incorporate agents' representative information to mitigate the environmental non-stationarity without raising dimensionality challenge, while safeguarding the commercial confidentiality of CSP agents. To foster more efficient learning coordination in the highly uncertain CPTN environment, a sequential update scheme is also developed to achieve monotonic policy improvement for CSP agents. Case studies on an illustrative and a large-scale test system reveal that the proposed method facilitates sufficient competition among CSP agents and corroborates the core benefits in terms of reduced charging costs for EV users, enhancement of RES absorption and cost efficiency of the power distribution network. Results also validate the excellent generalization capability of the proposed method in coping with CPTN uncertainties. Finally, the rationale of the proposed attention mechanism is validated and the superior computational performance is highlighted against the state-of-the-art methods.

Simulating Generalised Locational Marginal Pricing for Power Markets in East Africa

There is an enormous opportunity for adopting a wholesale electricity market under locational marginal pricing. Besides ensuring competition and accounting for network congestion, the price signal reflects a space-time electricity economy. This paper examines the prospects of introducing a single East African electricity market in five selected partner states. It reports on a pioneering effort to simulate the coupling of power markets, estimate locational marginal prices, and determine the economic benefits of cross-border power trade and competition across these five countries. The nodes representing the primary power systems in these five countries are simulated in an optimal power flow model using optimisation software GAMS. The total welfare of electric power market integration is estimated at $ 4.8 million/hr, representing a welfare gain of $ 2.6 million/hr, thus, net total welfare increases by 118%. The simulation also investigates the implications for locational prices and power flows under constrained and unconstrained scenarios. To secure the benefits of an integrated electricity market, it is vital to pursue significant institutional restructuring, a robust transmission infrastructure, and the harmonisation of energy policies.

Market Integration among Major Wheat Markets in India

The study is based on secondary data which tests the price movement and integration between major national wheat markets i.e. Lakhimpur, Agra, Vidisha, Dewas, Kota and Baran markets. Monthly wheat prices data of 15 years from 2007-08 to 2021-22 which was collected from www.agmarknet.gov.in. To analyze the market integration among major wheat markets, the Augmented Dickey-Fuller test, Johansen Co-integration test and Granger Causality test were applied with the help of E-views statistical package. The results indicated the strong market integration in terms of price linkages among major wheat markets. The results revealed that the major markets of wheat have strong price linkages and thus are spatially integrated. It could be seen that existence of mostly (10) unidirectional causality with few (3) bidirectional causality among selected wheat markets. It was further revealed from the analysis that, any disturbance in price will get corrected in about 5 days in Lakhimpur market, 3 days in Agra market and 3 days in Kota market in short-run equilibrium. The transfer of price signals from one market to another helps in stabilizing the prices over space and create a healthy competitive environment. This would also help to protect the interest of producer-sellers. In order to achieve the goal of integration, the government should strengthen the market intelligence and communication within markets. The following study recommendations were made: a) Government should subsidize provide is transportation. b) Government and stakeholder should advise commercial banks to give low interest long-term loans to farmers because many impoverished farmers can't afford crop mechanization. c) Government and other i.e, NGO advertising should be increased to raise knowledge about improved agricultural machinery. d) Government and other i.e, NGO should educate farmers. Farmers can employ technology if they are trained and educated. They should also be told how much fertilizer to use so it yields more.

Have the pilot carbon markets achieved synergistic control of carbon abatement and pollution reduction? The quasi-experimental evidence from China

The Chinese government has emphasized the necessity of synergistic control for carbon abatement and pollution reduction in the protection of eco-environment, as part of the 14th Five-Year Plan and the Long-range Objectives Through the Year 2035. As an important tool to reduce CO2, the carbon market can also affect the air pollution synergistically. The study utilizes the multi-period difference-in-difference (DID) model to analyze the impact of carbon market on carbon abatement, as well as the synergistic effect on different air pollutants. The study analyzes the mechanisms for carbon market to achieve synergistic control from the perspectives of “quantity” and “category” comprehensively. The findings indicate that: (1) carbon markets can reduce CO2 significantly, with a synergistic effect mainly observed on SO2 and soot (dust). These results remain robust when focusing on the industrial sector or adopting the PSM-DID estimation, etc.; (2) the mechanisms to achieve synergistic control vary among different air pollutants. Promotion of technology can only synergistically reduce SO2. Optimization of energy consumption structure is the top priority for reducing CO2. The most effective mechanism to achieve synergistic control of air pollutants is improvement of energy utilization efficiency; (3) to achieve synergistic control goals, the Chinese national market should prioritize industries that release both CO2 and NOx in the rolling-out plan, enrich the trading tools, and formulate effective price signals.