Price Signals Research Articles

Long-term issues with the Energy-Only Market design in the context of deep decarbonization

There has been fierce controversy in the literature over the long-run efficiency of the energy-only market (EOM) design ever since its inception. In this paper, we provide novel insights to illuminate this historical controversy, and we revisit it with a focus on contemporary issues and the profound changes brought about by the energy transition. Specifically, we develop an analytical and modeling framework to quantitatively investigate how EOM outcomes hinge on the underlying behavioral, informational and structural assumptions. We apply our framework to a case study calibrated on Californian fundamentals that captures the key features of energy systems under deep decarbonization. We characterize how EOM outcomes can substantially deviate from the long-run optimum as soon as one assumption is relaxed compared to theoretical requirements. This leads to pathways with higher electricity prices, lower security of supply and delayed decarbonization. In particular, we highlight how market price signals alone are prone to a dynamic entry-exit coordination problem between investment in low-carbon assets and the phaseout of fossil-fired assets. This calls for a market design reform to complement price signals that accounts for realistic assumptions.

A Linked Swing Contract Market Design With High Renewable Penetration and Battery Firming

As renewable generation and energy storage systems become increasingly cost-effective, modern power systems are increasingly reliant on variable energy resources (VERs) to meet demand. However, the variability and uncertainty of VERs' generation require additional flexibility to mitigate their impacts. Unfortunately, the current power market designs rigidly define different power and reserve products, making it difficult to fully recognize and compensate for this flexibility. Furthermore, since the marginal operating costs of the system quickly approach zero, current marginal cost-based market mechanisms struggle to provide price signals that allow independent power producers to make informed decisions about constructing, operating, and retiring plants. To address these issues, this paper proposes a new linked electricity market design based on swing contracts for operating power systems with high penetration of VERs and energy storage resources. The feasibility of the proposed approach is demonstrated through study results on the modified IEEE 118-bus test system.

Market design for ancillary service provisions of inertia and frequency response via virtual power plants: A non-convex bi-level optimisation approach

The Great Britain (GB) government is paving the path to decarbonisation by actively promoting the integration of wind power into its generation mix. This sharp transition to renewable energy, however, introduces specific challenges. The characteristics of non-synchronous wind turbines have the potential to impact grid stability and frequency security due to the reduction in system inertia. In response to these challenges, the deployment of virtual power plants (VPPs) is envisioned in deregulated power systems, which allow for the aggregation and coordinated control of diverse distributed energy resources, enhancing grid flexibility, reliability, and efficiency. Moreover, VPPs offer the provision of system inertia and frequency response services at the national level. This study drops this assumption and leverages the localised flexibility inherent in VPPs to meet the ancillary service requirements of the low-carbon power system. The formulated problem is constructed as a non-convex bi-level optimisation problem, where the upper-level problem represents the operation of a frequency-constrained unit commitment model, and the lower-level problem embodies the energy dispatches and frequency responses of a group of VPPs, guided by the dual price signals of cleared energy and ancillary services. To address the non-convex nature of the unit commitment problem, a two-fold approach is employed. First, binary commitment status decision variables are relaxed to continuous versions. Subsequently, the duality gap between the original non-convex unit commitment problem and its relaxed dual form is minimised, yielding a solution closely approximating the optimal solution of the original problem. Second, the relaxed bi-level optimisation problem is transformed into a single-level mathematical programs with equilibrium constraints by replacing the lower-level VPP problems with their equivalent Karush-Kuhn–Tucker optimality conditions. The case studies conducted in this work encompass a comprehensive scope. Initially, the study evaluates the effectiveness of VPPs in delivering ancillary services within the projected context of the GB power system for 2030. The findings reveal a substantial 28.28% decrease in system operation costs when compared to the benchmark case lacking VPP flexibility. Additionally, the integration of VPPs leads to significantly lower price signals for inertia, primary frequency response, and enhanced frequency response, reduced by 81.21%, 79.13%, and 86.27%, respectively, in contrast to the benchmark case. The analysis then delves into a sensitivity exploration, investigating the inherent flexibility of VPPs. The profit of local VPPs is expected to increase with VPP flexibility. Finally, the study illustrates how VPPs can adapt to and derive benefits from an increasing level of wind power penetration by providing a higher amount of frequency response.

Retail competition among multi-type retail electric providers in social networks

Liberalization of the electricity retail market has intensified the competition among different types of retail electricity providers (REPs), leading REPs to pay more attention to electricity consumers' (ECs') preferences for electricity service quality, such as reliability and flexibility. In addition, social learning also greatly influences consumers' electricity purchase strategies. Therefore, this paper models the price and service quality competition among multi-type REPs in a two-layer electricity retail market network that integrates electricity trading and consumer social learning. Firstly, we construct a two-layer network that transforms the dynamic process of ECs switching REPs and changing neighbors into the reconfiguration of the network structure. The upper layer is a bipartite network to express the dynamic transaction between REPs and ECs, while the lower layer is a random network to represent the learning interaction between ECs. Secondly, considering network externalities, we model a retail competition based on price and ECs' preferences for electricity service quality in the network above and get the equilibrium solutions in different scenarios by simulating the electricity consumption data of 600 London households. The results demonstrate that network externalities hinder the transmission of price signals, and the density of the EC network affects the market game result, while the update speed of the EC network has little effect. This study provides theoretical support for multi-agent decision-making in the electricity retail market.

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

AbstractDeep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

From Local Energy Communities towards National Energy System: A Grid-Aware Techno-Economic Analysis

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low-voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom–up approach. District energy systems with a building scale resolution are modeled in a mixed-integer linear programming problem. The Dantzig–Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of a renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale reveals a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Placed in perspective with the optimal PV capacity forecast at 15.4 GW peak at the national level, coordinated investment between local and national actors is needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems from 12% to 83% and curtails 48% of local renewable electricity.

Carbon price signal failure and regulatory policies: A systematic review

The current failure of carbon price signals is hindering the orderly operation of the carbon market. Based on a statistical review, this study extends the systematic theoretical framework of price signal failure. It was found that: (1) the government is a key creator of demand and supply, and cannot play the role of night watchman as in general commodity markets; (2) information asymmetry, systematic supply-demand deviation and excessive intervention all account for price signal failures; (3) the boundaries of carbon price failure are determined by a combination of factors, including market efficiency, probability of carbon quota shortage, and carbon intensity. In order to better respond to such failures, this study not only proposes an innovative theoretical analytical framework for selecting 12 policy tools, but also presents additional viewpoints based on real-world experience. While mixed tools are more cost-effective, political acceptability, a combination of mixed tool risk, excessive intervention, uncertainty, carbon leakage and technology spillovers are significant issues that cannot be overlooked. These novel findings provide a reference for governments around the world to judge the impact of policy tools in a comprehensive way.

Do China's pilot emissions trading schemes lead to domestic carbon leakage? Perspective from the firm relocation

The limited geographical coverage of pilot emissions trading schemes (ETS) raises domestic carbon leakage risks. Since relocation represents a primary channel that causes carbon leakage, this paper investigates the impact of China's pilot ETSs on firms' cross-provincial relocation with a method combining propensity score matching and staggered difference-in-differences. The empirical results, based on the data of Chinese listed industrial firms from 2009 to 2020, show that the ETS has accelerated firm relocation by increasing negative attention and social capital investments. According to the threshold analysis, firms with sufficient abatement efforts do not undertake relocation strategies. The theoretical analysis can also be confirmed by further investigations on the price signal distortions and the negative impact of relocation on carbon prices. Moreover, the study also demonstrates the heterogeneous effects across firm characteristics, industry types, and policy design features. Overall, these findings provide a reference for China's government to prioritize the establishment of domestic leakage prevention mechanisms for ETS.

Introducing wetland offset markets under development-restoration conflicts: The role of public offset credit supply

Wetland offset markets (WOMs) are increasingly applied worldwide as powerful tools for mitigating conflicts between wetland development and restoration. Reducing benefit uncertainty is key to promoting private restoration and introducing WOMs, which necessitates sufficient and stable price signals. Given that governments are important suppliers in WOMs, this article aims to explore the role of public offset credit (OC) supply in delivering and adjusting price signals during WOM formation and evolution. A general spatial agent-based wetland offset market model is built to simulate landowners' behavior, price dynamics, and WOM evolution under different public OC supply schemes. The results show that the spontaneous formation of WOMs is a time-consuming process. Price signals of public OCs reduce price fluctuations at the early stage of WOMs. This price stabilizing effect can cause a long-term reduction in benefit uncertainty perceived by landowners. Therefore, public OCs can facilitate WOM formation either through the supply side with high supply prices or through the demand side with low supply prices. During the entire WOM evolution process, due to landowners’ readaptation, cheap public OCs can cause significant market fluctuations following the ceasing of cheap public supplies. The impacts of public OC on wetland development and restoration might change over time, and the suitability of public OC supplies under different long-term wetland management preferences was analyzed. These findings can further the understanding of the process of introducing a new market mechanism, such as WOMs, and the role of the government as a supplier. The research results provide insights for WOM practices, public restoration and OC supply scheme design, and wetland development-restoration conflict coordination.

Price signal or blockchain technology? Quality information disclosure in dual-channel supply chains

Quality information asymmetry regardless of online or offline channels have damaged the profits of high-quality manufacturers. Price signal and blockchain technology (BCT) are two strategies to eliminate this asymmetry, with BCT potentially incurring higher costs but also boosting demand potential. Therefore, in a dual-channel supply chain consisting of a manufacturer with private quality information and a retailer, we use a game-theoretic model to investigate which information strategy should be adopted by the high-quality manufacturer. Main results unveil a counterintuitive finding: the high-quality manufacturer may not reap greater benefits from BCT despite its ability to simultaneously boost prices and demands in both channels; conversely, the retailer can still derive benefits from BCT even if it fails to augment the expected demand in the retail channel. We attribute this phenomenon to two key effects of BCT: the information effect and the basic demand expansion effect. Additionally, we highlight the pivotal impact of channel market share on the value of these two strategies, as it not only affects the efficiency of information transmission through the price signal strategy but also the distinct demand enhancement that BCT offers to both channels. Specifically, for the products with a lower production cost difference and a higher market share in the retail channel, our research demonstrates that BCT has the potential to benefit the entire supply chain. This paper provides practical guidance for the strategic implementation of BCT in dual channels.

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach

This work presents a distributed robust operation framework for the microgrids within the power distribution network. It addresses the uncertainties in photovoltaic generation, as well as active, and reactive loads in the network, by modeling them using polyhedral sets. The energy management problems are formulated as two-stage optimization problems in microgrids and power distribution networks for which Benders decomposition is used to solve the problems. The power flow constraints are relaxed using second-order cone relaxation and moment relaxation techniques in the power distribution network and microgrid energy management problems, respectively. The interaction between the microgrids and the power distribution network is represented using the price signal and the exchanged active and reactive power at the microgrids’ points of common coupling. The effectiveness of the proposed framework is shown using two case studies on IEEE 37-bus and IEEE 123-bus systems. The effect of the energy storage system on the operational cost of the system is assessed. It is shown that total operation cost is decreased by 9.69% when the energy storage system is integrated into the power distribution network. Additionally, the solution derived from the robust problem is compared with that of the deterministic problem.

Research on intelligent energy management method of multifunctional fusion electric vehicle charging station based on machine learning

The machine-learning based approach to energy management of multifunctional charging stations that meets the needs in the context of "carbon neutrality". The method takes multifunctional charging station as the research object, considers the uncertainty elements such as market price signal, charging load, and the change of photovoltaic power generation, and aims at maximizing the comprehensive operation efficiency of charging station in the decision cycle, realizes the prediction of power generation and load power by using deep learning method, and obtains the optimal energy management strategy by reinforcement learning, and realizes intelligent management and operation of multifunctional charging station. A typical case study is presented and simulation analysis is carried out for typical cases to verify the correctness and effectiveness of the method. It is shown that the method can effectively cope with the uncertain elements in the operation of multifunctional charging stations, reduce the hardware configuration and manual operation and maintenance costs of charging stations while ensuring safety constraints, and effectively improve the energy utilization efficiency and economy of the whole charging station.

Comparative analysis of bidding strategies for auction-based local energy markets

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.

A data-driven methodology to design user-friendly tariffs in energy communities

In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users’ preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities.

Enhancing the smart readiness of buildings: Combining Collective intelligence and Reinforcement learning in Building Energy Management

This research introduces a novel Energy Management approach, named CIRLEM, aiming to enhance the smartness of buildings by focusing on technical systems operations, environmental variations, and occupants' needs. Deployed in a simulated environment using Building Performance Simulation and Python integration, the study evaluates CIRLEM's performance under future extreme cold weather scenarios, employing a set of representative climate data. The pilot case, two building blocks in Sweden, undergoes assessment for energy demand, peak power, and thermal comfort. Results indicate that CIRLEM, particularly when driven by demand and price signals, effectively reduces energy demand and costs, demonstrating strong adaptability to extreme weather conditions. Thermal comfort is maintained regarding the temperature limits and variations. Ongoing developments attempt to refine the reward function and signal generation for thermal comfort enhancement and real-world implementation.

Using Price Signals to Better Manage Water Use

Using Price Signals to Better Manage Water Use

Learning from Ambiguous Price Signals

Learning from Ambiguous Price Signals

Design and Empirical Research on Cross-provincial and Cross-regional Power Transmission Pricing Mechanism Adapted to Marketized Trading in China

With the deepening of China’s electricity market reform, the cross-provincial and cross-regional power trading system dominated by government pricing has struggled to release reasonable price signals. Under a single electricity price system, transmission costs are prone to exceed the price difference between sending and receiving ends, thereby affecting the conclusion of cross-provincial and cross-regional transactions. In order to connect cross-provincial and cross-regional power transmission prices with the electricity market effectively, in this paper, a two-part transmission pricing mechanism is designed for cross-provincial and cross-regional power transmission “benchmark + floating” trading prices, and a price calculation model is established by selecting typical projects for empirical calculation to verify the effectiveness of the transmission pricing mechanism design. This research can provide reference for improving the pricing mechanism, which is conducive to promoting cross-provincial and cross-regional transactions.

Frequent Auctions for Intraday Electricity Markets

Continuous trading is currently becoming the standard for intraday electricity markets. In this paper, we propose frequent auctions as a viable alternative. We argue that batching orders in auctions potentially leads to lower liquidity cost, more reliable, less noisy price signals, and allows for better alignment of market outcomes with the technical realities of the transmission grid. In an empirical study, we compare the German continuous intraday market with counterfactual outcomes from frequent auctions. We find that traded volumes tend to be higher for continuous trading; however, the auction market benefits from lower liquidity costs and less noisy price signals. Furthermore, we critically discuss the suitability of continuous trading in the presence of network constraints and technical restrictions of conventional units. Taken together these findings suggest that in sparsely traded intraday markets, pooling orders in frequent auctions may be beneficial.

Serving two masters – How dual price signals can undermine demand flexibility

Demand-side flexibility is often encouraged via demand response programs, where users are incentivized to adjust their electricity consumption based on price signals in tariffs. The design and implementation of these signals are vital, as they link conditions of the energy system with user behaviour. As a result of energy system separation into stages of generation, many users are required to simultaneously respond to two price signals. While existing research has mainly examined responses to a single signal, this study highlights the complexities when users must react to two signals simultaneously. It presents a case study using two distinct price signals: real-time pricing (RTP) and a demand charge, analysing the implications such dual price signals could have on users. The study reveals the empirical incompatibility of these signals, leading to conflicting user goals. Such interference complicates decision-making for demand flexibility, potentially eroding user trust in retailers and system operators. The study ends with proposing an alternative tariff, resolving the dual price situation, which facilitates user comprehension and decision-making, and thus enhances the potential for demand flexibility.