Price Signals Research Articles

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy.

Demand Response Potential of an Educational Building Heated by a Hybrid Ground Source Heat Pump System

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.

Integrated market scheduling with flexibility options

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators’ coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities.The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators’ coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation.The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems.

Price matching in online retail

AbstractWe analyze a sample of consumer‐electronics products sold by the US NewEgg online‐retailer to study the impact of price‐matching guarantees (PMGs) on prices. By applying a difference‐in‐differences approach, we find that the price of the policy‐adopting retailer increases by 4.7% during the policy validity period and up to 5 days after the treatment, while those of the non‐adopting competitor are not affected. Results are mainly driven by highly‐rated, visible, and expensive products, whereas the policy does not affect low‐rated, less visible, and cheaper products. These findings are consistent with the hypotheses that PMGs act as price discrimination or signaling tools.

The effect of house price constraint conditions in land granting on the second-hand housing market: evidence from Beijing, China

ABSTRACT To curb the surge in housing prices, Chinese governments have introduced various measures in the real estate market. House price constraint conditions (HPCCs) are conditions for sale in land granting which restrict the selling price of newly built houses. Such unique practice has been used as price regulation and signal in many cities with little understanding of its impact. This study aims to investigate the effectiveness, mechanism, and cost of HPCCs on the second-hand housing market for policy improvement, completing existing literature primarily focusing on housing policies. We collected data on land granting and second-hand house transactions in Beijing, which was the first to implement HPCCs. Employing difference-in-difference and other empirical techniques, we find that land granting with HPCCs alters buyers’ and sellers’ behaviours and expectations, leading to a significant decrease in the price level and growth rate. However, HPCCs are associated with lower supply, increased speculation, inequality issues, and potential loss of fiscal revenue. Policymakers need to improve HPCCs while considering market-oriented regulation tools.

Forecasting of price signals using deep recurrent models

Forecasting of price signals using deep recurrent models

EXTRACTING PRICE SIGNALS FROM THE TERM STRUCTURE OF COMMODITY AND CREDIT MARKETS USING AUSTRIAN BUSINESS CYCLE THEORY

Austrian School economists first explained the significance of interest rates for the structure of production in time. Furthermore, they showed how manipulating the level of interest and the credit supply causes the business cycle (v.Böhm-Bawerk, 1891; v.Hayek, 1938; v.Hayek, 1941 and v.Mises, 1949). More recently, Austrian School economists have elaborated the relationship between the term structure of interest rates and the business cycle (Barnett and Block, 2009; Bagus and Howden, 2010; Bagus et al., 2018; Potuzak and Nemec, 2020; Alonso-Neira and Sanchez-Bayon, 2023). At the same time, market practitioners understand the potency of yield curve inversions as a predictor of future recessions as a result of experience and observation unrelated to Austrian Business Cycle Theory (ABCT). Reference textbooks (Stigum and Crescensi, 2007) explain that capital allocators are pricing in future cuts to the central bank discount rate, thus inverting the yield curve. This ignores the role of free-market price signals in credit, which result from supply and demand fundamentals, and are the link connecting ABCT to term structures.The term structure of commodities markets is also affected by actual or anticipated shortfalls in production over different time horizons. This is widely understood, perhaps because settlement by physical delivery is commonplace and tangible (Hull, 2022). Even though credit and derivatives may at first glance seem more abstract, the same underlying market principles are at work there. The theoretical foundations of and recent work on ABCT provide the necessary tools to analyse both markets through the same lens.

Additional revenues estimation in a market-based redispatch: An opportunity for flexibility

The technical constraints in the transmission networks are exerting significant strain on the functioning of electricity markets as the share of variable renewable energy sources is increasing. To manage this issue, the transition to a nodal pricing market has been proposed in the literature. Given that the current structure of the European Union electricity market corresponds to a zonal pricing, the transition to nodal pricing would imply fundamental changes in its market structure. In order to avoid the latter, this study develops a method to identify locational price signals in a decentralized market with zonal pricing and subsequent market-based redispatch. To this end, first, nodes with structural technical constraints are identified –using energy programmes–. Then, the additional revenues of physical units located at these nodes are estimated –using electricity prices–. Finally, this strategic information will guide the development of flexibility solutions –manageable generation capacity and demand response– in these geographic locations, mimicking the main advantage of nodal pricing. Our methodology has been empirically tested on the Spanish power system, using hourly data from the System Operator and Market Operator for four full years, from 2019 to 2022. The results show a higher and constant economic revenue in certain nodes of this system –upward redispatch price is 74.90% higher than day-ahead market price–. Thus, this method can be used by market participants to evaluate publicly available data from a decentralized market with zonal pricing and subsequent market-based redispatch, and obtain essential information to make the best investment decisions.

The energy crisis differentially impacted Swiss and German citizens’ energy literacy and efficiency preferences but not their support for climate policies

The energy crisis reshaped energy politics, resulted in energy price surges, increased energy capacity, and large-scale energy-saving campaigns. While an energy demand reduction was observed in Europe, little is known about how the crisis impacted citizens’ climate-relevant judgment and decisions. Here we report a preregistered two-wave cross-national study (N = 1040) spanning the winter of 2022/2023 to investigate how support for renewable energy policies, energy literacy, and energy-efficient product choices developed as this crisis unfolded. We couple longitudinal experimental data with real-world energy price data. This natural experiment indicates that energy-efficiency preferences were impacted by the salience of the crisis and real-world price signals. Changes in energy literacy were contingent on the numerical abilities of participants, suggesting that the crisis increased knowledge gaps between different groups in society. Support for renewable energy policies, and prioritization of key policy attributes, remained stable in Germany and Switzerland despite shifts in public attention and uncertainty.

Vertical price transmission in agricultural markets in Ghana

The purpose of this study was to investigate the extent of price transmission along the agri-food supply chain in selected regions in Ghana. The extent of transmission was used to assess the efficiency and effectiveness of the agricultural marketing systems in the area. Efficient marketing systems are expected to reduce the issue of regional price fluctuations, encourage fair income distribution throughout the supply chain, and improve food security. The preliminary time series stationarity/unit root and seasonality tests were performed using the Augmented Dickey-Fuller test (ADF)/Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) and Canova-Hansen seasonal stability tests respectively. Following the detection of statistically significant threshold effects by the Supremum Likelihood Ratio (Sup-LR) tests, the extent of price transmission was investigated using two-region threshold vector error correction models. The initial tests found that the monthly wholesale, retail, and producer price series for maize, cowpea, and yam used in the study were first difference stationary rather than seasonally integrated. The findings revealed that price signals in the agri-food markets were not fully transmitted across the supply chain. Thus, the extent of price transmission was generally low, creating unexploited arbitrage opportunities along the chain. The study identified high transfer costs (proxied by the threshold parameter) and actor-specific market power (as revealed by the Granger causality test) as the primary obstacles to effective market functioning in the area. Minimizing transfer costs through infrastructural development such as improvements in road networks linking farming communities and markets, access to price information, and regular stakeholder engagements could help improve the functioning of the agri-food markets.

The impact of energy prices in decarbonizing buildings’ energy use in the EU27

The recent surge in end-use energy prices in the EU has intensified the debate on how price signals influence the decarbonization of the building sector, a key contributor to global energy consumption and emissions. This study investigates the role of energy price signals in driving the decarbonization of the EU-27 building sector using the Invert/EE-Lab model to simulate various scenarios up to 2050. The analysis examines the elasticity of energy demand, particularly how different price scenarios impact investment decisions in energy-related technologies. The findings reveal that a 10 % increase in energy prices can lead to a reduction in energy demand by up to 3 %, depending on the scenario. Furthermore, price elasticity varies across different policy and price settings, with stricter regulations resulting in lower elasticity. The results underscore that while stringent regulatory measures can achieve significant energy savings and reduce greenhouse gas emissions, they also limit the responsiveness to price signals, with heat pump shares reaching 47–86 % in high-efficiency scenarios. Conversely, more moderate policy measures increase price elasticity, leading to 15–55 % heat pump shares. This study highlights the importance of better understanding price elasticity in energy modeling to project better the outcomes of policy interventions aimed at achieving a low-carbon energy system.

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Maturity assessment of grid-scale flexibility and energy storage services towards a decarbonized Europe

Background The study evaluates the maturity of grid-scale flexibility services, essential for Europe's transition to a decarbonized economy. Conducted under the Horizon-Europe SINNOGENES project, it focuses on energy storage technologies, regulatory frameworks, and stakeholder perspectives. Methods The methodology comprises three phases. First, significant grid-scale energy storage and flexibility technologies, including Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G) systems, were reviewed. Second, the regulatory framework for flexibility services at the EU transmission systems level was assessed, analyzing key EU directives and national regulations from Spain, Portugal, Germany, Cyprus, and Switzerland. Third, stakeholder perspectives were gathered through a survey of forty-four stakeholders in the European energy sector and bilateral interviews with companies and organizations in the energy sector. Results Technological advancements in grid-scale flexibility services have been identified, along with persistent regulatory challenges. The survey and interviews revealed a varied perception among stakeholders regarding the maturity and implementation of these services. A high demand for flexibility was noted, with 77% of stakeholders expressing a need for or active use of flexibility services. Electrochemical energy storage and demand response were highlighted as the most relevant technologies. However, high implementation costs, perceived insufficient benefits, and regulatory uncertainties were major barriers to widespread adoption. Conclusions The study concludes that despite notable technological progress and some regulatory advancements, several barriers still impede the full maturity and integration of grid-scale flexibility services in Europe. Addressing these regulatory and market challenges is crucial for achieving a fully functional and integrated flexibility service framework across the European grid. Harmonized regulations, financial incentives, and clear long-term pricing signals are essential to support the market.

Price spillovers and interdependences in China's agricultural commodity futures market: Evidence from the US-China trade dispute

We examine the effects of the US-China trade dispute on price spillovers and interdependencies among the ten most liquid agricultural futures contracts traded on China's commodity exchanges by employing the time-varying connectedness framework modified by Antonakakis, Chatziantoniou, and Gabauer (2020). We find that the overall market frictions increase modestly due to the clustering of mixed informational disturbances caused by the trade dispute. This lowers the gross spillovers in the entire asset system by reducing the efficiency of general price signal transmission. Notably, subsidiary products of rapeseed and soybean contribute the most to the total and net directional spillovers in the whole market, acting as shock transmitters before the trade dispute. The RBD palm market gains a dominant role as a shock transmitter during the trade dispute and its role strengthens further after the trade agreement was reached. Additionally, the average intensity of pairwise spillovers declines considerably during the trade dispute, suggesting weakening asset interconnectedness. The responsiveness of shock transmission channels to the trade dispute impacts is found to vary across industrial chains and the presence of delayed economic effects is detected in the structural changes of market interdependences.

Extension of a low-tech Model Predictive Control (MPC) algorithm for grid-supportive heat pump operation

This paper presents the further development of a low-tech Model Predictive Control (MPC) for the grid-friendly operation of heat pumps. The developed algorithm is already available in the literature [1] and successfully implemented in multiple buildings. The overall goal is, to predictively optimize comfort parameters taking into account weather forecast data for 48 h. A simple mathematical building model and an optimization function are used to calculate the heating (or cooling) requirement and determine the optimum heating (cooling) output curve over time.Based on the initial results, the algorithm is extended to include an energy price forecast. This modification enables the simultaneous optimization of comfort and heat supply costs. This forecast-based cost & comfort function is designed for the use of heat pumps in buildings with thermally activated components (TAB). The extension enables the MPC to process day-ahead electricity prices or other price signals in addition to weather forecast data. This means that the operation of heat pumps can be shifted to periods that are beneficial to the grid.The algorithm is analysed and validated in a Matlab/Simulink building simulation for a heating period of one sample month. A case study with different price scenarios is the main part of this paper. Depending on the assumed price fluctuation, the simulation leads to cost savings between 6.65 % and 12.5 % for the observation period of one month. The simulation results thus show that heat generation can be shifted to times with lower electricity prices, which leads to a significant reduction in heating costs without any significant loss of comfort. In conclusion, the developed low-tech MPC enables grid-supportive operation of heat pumps.

Carbon pricing under electricity market constraints: analyzing rent management dynamics in the Republic of Korea

ABSTRACT How do policymakers manage the tension between enforcing a carbon price and ensuring low-cost electricity provision? This paper answers this question in two steps. First, it combines literature on electricity market design and rent management to arrive at a novel analytical framework. Carbon pricing can disrupt longstanding rent transfers between the state and electricity consumers by making it more expensive to produce and/or consume fossil fuel-based electricity. We theorize that policymakers respond to this dilemma by making incremental policy design adjustments to gradually enforce rent losses across the electricity production value chain. We apply this framework to study the implementation of the Korean emissions trading scheme (ETS) policy 2015–2022. Drawing on 20 expert interviews and policy documents we identify an initial sequence of policy design adjustments which enable, but also delay, the enforcement of the carbon price signal in the electricity system. This incremental and politically contentious process reduces the ETS policy’s ability to abate greenhouse gas emissions from the Korean electricity sector in the short term. The findings suggest that the effectiveness of carbon pricing in the electricity sector hinge on the parallel construction of supportive institutions to permit meaningful carbon cost pass-through.

How to implement the 2023 IMO GHG strategy? Insights on the importance of combining policy instruments and on the role of uncertainty

In its 2023 strategy for reducing GHG emissions from ships, the International Maritime Organization aims for the uptake of zero or near-zero GHG emissions technologies, fuels or energy to represent at least 5 %, striving for 10 %, of the energy used by 2030. Measures proposed to reach this target combine two kinds of policy instruments: an emission intensity standard and GHG emissions pricing. Proposals vary in terms of the design of the pricing scheme and the subsequent uncertainty that would affect the price signal. This paper assesses the extent to which emissions pricing effectively complements a more restrictive standard on the intensity of GHG emissions to boost transition to low-carbon vessels. Attention is paid to the role of the volatility of the price signal of GHG emissions and fuels prices. For this purpose, the paper applies a multiple real options model to the decision to renew a ship under various sources of uncertainty. Simulations carried out based on a case study show that the price signal effectively complements the more restrictive standard but that the decarbonization objectives are only achieved with a quite ambitious level of pricing. The role of the volatility of the price signal of GHG emissions appears negligible compared to other sources of volatility.

Impact of different CO2 price paths on the development of the European electricity system

In energy system transformation studies, the assumption of linearly rising CO2 prices is common and based on the decreasing issued European allowances (EUA). The actual auction price for allowances is influenced by market dynamics, resulting in non-linear variations in CO2 prices due to the possibility of banking allowances. This study explores the impact of non-linear CO2 price trajectories on the European electricity market. Various scenarios, such as increasing prices with the market interest rate, early price peaks, collapse, or fluctuations, are examined until 2030. The study quantifies the effects on power plant investments, profitability, consumer costs, and government revenues. The findings reveal that early and high price signals lead to an earlier transition with 7 % more wind power plants but at 4 % higher costs. Conversely, lower initial prices delay power plant investments in wind power plants by 6 % and result in 21 % higher emissions. Compensation for climate impact costs can yield a positive cost-benefit analysis in scenarios with early and high price signals when emissions are avoided through mechanisms like the Market Stability Reserve (MSR).

Why Survey-Based Subjective Expectations Are Meaningful and Important

For decades, households’ subjective expectations elicited via surveys have been considered meaningless because they often differ substantially from the forecasts of professionals and ex-post realizations. In sharp contrast, the literature we review shows that household characteristics and the ways in which households collect and process economic information help us understand previously considered puzzling facts about their subjective expectations. In turn, subjective expectations contribute to explain heterogeneous consumption, saving, investment, and debt choices as well as different reactions by similar households to the same monetary and fiscal policy measures. Matching microdata on households’ characteristics with the price signals the same households observe, their subjective expectations, and their real-world economic decisions is crucial to establishing these facts. Our growing understanding of households’ subjective expectations inspires several theoretical and empirical research directions and begets the design of innovative and more effective policy instruments.

Enhancing electric vehicle charging efficiency at the aggregator level: A deep-weighted ensemble model for wholesale electricity price forecasting

The proliferation of electric vehicle (EV) adoption strains low-voltage distribution networks, particularly in aggregated charging scenarios, prompting utility companies to incentivize charging aggregators for optimizing load balancing within thermal limits. These aggregators utilize machine learning algorithms to understand electricity price signals and orchestrate the optimization of the EV charging process. However, conventional machine learning approaches fall short when dealing with the dynamic and volatile nature of electricity prices, emphasizing the necessity for advanced ensemble models. This paper introduces a novel Deep-Weighted Ensemble Model (DWEM) rooted in standard and stacked Long Short-Term Memory (LSTM) networks designed for wholesale electricity price forecasting, to manage the EV charging at the aggregator level. The ensemble development process involves developing an architecture that highlights the significance of the DWEM model in supporting aggregators for the charging optimization of EVs. The charging optimization problem of aggregated EVs is formulated, and the heuristic mechanism is systematically presented, evaluating various weight configurations, and selecting those characterized by the highest levels of accuracy to comprise the ensemble model. Moreover, we incorporated a standard deviation mechanism to evaluate the impact of the proposed DWEM on forecasting accuracy, mean squared error, and mean absolute error across various standard deviation levels. We leveraged a publicly available Houston electricity dataset and performed a detailed data engineering mechanism, accounting for data both with and without outliers. Subsequently, we applied the proposed DWEM to this dataset and conducting three types of comparative analysis: (a) evaluating model performance in terms of accuracy, mean square error, and mean absolute error; (b) assessing aggregator charging analysis focusing on charging load and cost; and (c) analyzing computational complexity and execution time. The simulation results demonstrated a improvement in accuracy and reduction in charging load and cost compared to state-of-the-art methods, while maintaining competitive computational complexity.