Real-time Pricing Mechanism Research Articles

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

Potential and challenges of Peer-To-Peer (P2P) electrical energy trading: investigation and case study

This paper explores the potential and challenges associated with Peer-to-Peer (P2P) electrical energy trading, focusing on both theoretical investigation and practical application through a comprehensive case study. By examining the interactions between prosumers (those who both produce and consume energy) and consumers, we employ utility functions to model these interactions. Optimization is achieved using a non-cooperative game theory approach coupled with real-time pricing mechanisms to reflect market dynamics accurately. Our analysis ensures feasibility and reliability by considering crucial factors such as energy balance and grid capacity constraints, which are essential for maintaining system stability. The simulation results demonstrate the effectiveness of our proposed method. These results highlight the significant potential of P2P trading to enhance overall energy efficiency, promote decentralized energy management, and reduce reliance on traditional energy sources. Furthermore, this study provides detailed insights into maximizing total utility by determining optimal prices and energy trades, ultimately showcasing the transformative impact of P2P trading on real-time energy market operations.

New Customized Bidirectional Real-Time Pricing Mechanism for Demand Response in Predictive Home Energy Management System

New Customized Bidirectional Real-Time Pricing Mechanism for Demand Response in Predictive Home Energy Management System

Demand side management with wireless channel impact in IoT-enabled smart grid system

Demand Side Management (DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing (RTP) mechanism and a wireless Neighborhood Area Network (NAN) with data transfer uncertainty. A Zigbee-based Internet of Things (IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

Distributed real-time pricing of smart grid considering individual differences

The utility function that characterizes customers’ satisfaction with electricity consumption plays an important and irreplaceable role in the real-time pricing mechanism based on the social welfare maximization model. Without the accurate quantification of customers’ utility, the real-time pricing will deviate from the reality. In fact, the utility functions of different types of customers in different regions should be obtained by fitting a large amount of historical data over a long period of time based on insight into the relationship between factors and utility. Based on the consideration of customers’ individual differences, a new utility function is proposed, which enriches the form of the utility function and provides a reference for fitting a real and accurate utility function. Further, based on this proposed utility function, a real-time pricing model of social welfare maximization is developed to obtain the fair electricity price between customers and the power supplier. On the basis of the separable structure of variables, we design distributed algorithms with global convergence for the pricing model and estimate a worst-case convergence rate. Numerical simulations verify the feasibility and effectiveness of our algorithms and the rationality of the new utility function, i.e., the electricity price based on the proposed utility function is more robust than the existing ones.

Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

A Hybrid Real-Time Electricity Pricing Strategy with Carbon Capture, Storage and Trading

During the power system low carbonization process, various policies and technologies for low-carbon have been rapidly developed. Real-time demand side management by energy suppliers based on real-time pricing (RTP) has become a reality via smart meter technology. Motivated by this, an RTP mechanism is used to guide users’ demand response, and the carbon trading regulation (CTR) and carbon capture, storage (CCS) technology are used on the supply side. Through these means, we can reduce carbon emissions from the power system and achieve the goal of carbon neutrality. Furthermore, a game model is used to construct the relationship between the energy supplier and users. Since the energy supplier acts first and then the user make their decision, the leader is the energy supplier and the followers are users. Further, to protect the information privacy of them and independently solve the goals of them, a distributed solution approach combining a differential evolutionary (DE) algorithm and Gurobi solver is developed for finding the equilibrium solution. The proposed model’s economic and environmental benefits are verified and the effectiveness of CCS technology and CTR is accessed via the numerical analysis, what means the proposed strategy can serve as a guide for the system’s low-carbon management.

Real-time pricing based on convex hull method for smart grid with multiple generating units

With the growing demand and complex supply structure for electricity, higher requirements are proposed for generating units to regulate the peak load, resulting in significant increases in the startup cost. These issues bring urgent need to develop new pricing mechanisms. The real-time pricing (RTP) is an ideal pricing mechanism for clipping peak load and balancing supply and demand. This paper designs an RTP mechanism for the smart grid which integrates multiple generating units on the supply side and distributed renewable energy generation devices and energy storage systems on the demand side. Focusing on the interests of both supply and demand sides, a social welfare maximization model that incorporates startup costs of generating units is formulated. To tackle the discontinuity in the proposed model, the convex hull method is utilized to transform the primal model and derive the convex hull price which is widely regarded as an optimal price that can reflect the aggregate cost of generating units but has rarely extended to RTP. The related dual theory indicates that the convex hull price can be obtained by the Lagrange multiplier of the proposed social welfare maximization problem associated with the supply–demand balance constraint. Moreover, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. Simulation results validate rationality and effectiveness of the proposed pricing mechanism.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Operational optimisation of integrated campus energy systems considering integrated demand response

Against the backdrop of the rapid development of the energy internet in the park, the multi energy coupling and complementary characteristics of integrated energy systems provide more space for optimizing the participation of demand parties in their coordinated planning. Establishing a effective demand side model with multiple energy flows and response types has become an effective means to improve system performance. For this reason, this paper takes the comprehensive energy system of the park with multiple electricity, gas, heat and cold complements as the research object, establishes a complete model of multi load and multi type demand response based on the improvement, incentive and substitution of real-time pricing mechanism, and uses multi-attribute decision-making method to obtain the optimal configuration of the system by establishing an optimization framework for iteration of the main problem and sub problem. The simulation results show that compared with traditional energy supply system configurations, the complete model established in this paper for various controllable resources on the demand side achieves the coordinated and complementary operation of the system’s multi energy and low-carbon economy, fully utilizing the regulatory potential of the demand side, effectively reducing load fluctuations and energy supply costs.

Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators

Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive.Funding: R. Belo acknowledges funding by Fundação para a Ciência e a Tecnologia [UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016], POR Lisboa, and POR Norte [Social Sciences DataLab, PINFRA/22209/2016].Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .

Energy management method for microgrids based on improved Stackelberg game real-time pricing model

With the rapid development of microgrids with distributed generations (DGs) and energy storage system (ESS), it is important to study energy management methods to improve the operation economy of microgrids. However, there is currently a lack of research on microgrid’s energy management models including multi-party groups such as wind turbines, photovoltaics and ESS. This paper proposed an energy trading management method of microgrids based on Stackelberg game real-time pricing mechanism, which can solve the more complex optimization operation problem of microgrids. First, the rolling optimization was carried out to determine the charging and discharging behavior of ESS for maximizing the total benefit microgrid in the next few time slots. Further, a Stackelberg game real-time pricing model was built. The electricity prices of different entities in the microgrid in the next time slot were optimized by microgrid operator (MGO) to determine the load demand of each DG, preference parameter in the utility function of DGs was improved to promote the internal energy interaction and the economic benefits of the microgrid. Finally, the results show that our method can effectively improve DGs’ total utility and stimulate energy trading within the microgrid. Compared with no optimization and traditional method, the daily profit of MGO obtained by our method was increased by 31.89% and 5.4% respectively, verifying the economics of the proposed method.

Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy

In this study, multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty in renewable energy generations is designed to obtain optimal energy point sets of microgrid. The proposed dispatching model contains upper-level model and lower-level model. The economic and environmental benefits of the microgrid are adopted as the optimization objectives in the upper-level model and the economic benefits under incentive-based demand response is taken as the optimization objective in the lower-level load demand. In the upper-level model, sequence operation theory is designed to deal with the uncertainty in renewable energy generations, and virtual real-time tariffs and electricity credibility of power consumption are utilized to make demand response by shifting load in the lower-level model. To optimally solve the proposed model, improved enhanced multi-objective optimization sparrow search algorithm (EMOSSA) based on sinusoidal search strategy, diversity variant processing by Cauchy variation and external archive updating mechanism is developed to optimize the objective functions of the upper-level model and mixed integer linear programming CPLEX is employed to determine the load demand for the lower-level model. After that, the power demand plan of the lower-level model and the real-time prices of the upper-level model are updated iteratively to obtain the optimal dispatching solutions using the real-time pricing mechanism. Eventually, experimental results of several operation scenarios illustrate the proposed optimal dispatching method can deal with the uncertainty of renewable energy generations and the randomness of load, thus, achieving a well supply and demand balance between microgrid and users.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

A real-time pricing mechanism considering data freshness based on non-cooperative game in crowdsensing

A fair and reasonable price mechanism is fundamental in improving the efficiency of the crowdsensing market. Being an emerging paradigm that utilizes widely distributed wireless communication network and smart sensing devices to collect sensing data, crowdsensing service offers a plausibility for collecting real-time data. However, previous studies ignored the complex competition among participants in the free data trade market and the fresh data demands of increasingly popular real-time applications. This paper introduces the age of information (AoI) as a criterion to measure the freshness of data geared towards constructing incentive models for the data provider and data requester. Subsequently, considering the opening of the market, we design a price mechanism framework based on non-cooperative games, which can help the crowdsensing platform provide each participant a stable sensing strategy to maximize profit. For the AoI-sensitive case, i.e., when the data quality deteriorates with the time. A co-evolutionary algorithm based on heuristic search is designed to solve the sensing strategy for each provider to achieve Nash equilibrium. Furthermore, for the case where AoI is insensitive. An improved relaxation algorithm is proposed to provide a stable strategy for the data provider. Numerical findings reveal that the developed algorithm can effectively identify stable sensing strategies in a Nash equilibrium.

An online hybrid mechanism for dynamic first-mile ridesharing service

This paper leverages the “Mechanism Design” theory to design the ridesharing-based transit feeder service with mixed scheduled and on-demand passenger requests. An online hybrid mechanism is proposed with four incentive objectives: promoting passengers to participate by satisfying their mobility preferences, inducing passengers to truthfully reveal their mobility preferences, incentivizing the service provider to be financially sustainable, and incentivizing more regular commuters to early schedule the service. We propose and prove four properties, “preference-based individual rationality”, “preference-based incentive compatibility”, “financial sustainability”, and “scheduling preferability” to achieve the four incentive objectives, respectively. This online hybrid mechanism is comprised of a dynamic re-optimization methodology for re-matching and re-routing and a hybrid real-time pricing mechanism discriminative against different passenger types. In order to obtain the large-scale solutions for the online hybrid mechanism, this paper improves the solution pooling approach (SPA), which was originally proposed in our previous work for a static offline mechanism, to adapt for the online hybrid mechanism. The improved SPA successfully sustains the “preference-based individual rationality”, “financial sustainability”, and “scheduling preferability” properties. The simulation results demonstrate the superiority of the proposed online hybrid mechanism over the static offline mechanism and the outperformance of the improved SPA over the original SPA.

Multi-Agent-Based Collaborative Regulation Optimization for Microgrid Economic Dispatch Under a Real-Time Price Mechanism

Multi-Agent-Based Collaborative Regulation Optimization for Microgrid Economic Dispatch Under a Real-Time Price Mechanism

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1&#x2013;3 while Scenarios 4&#x2013;6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>-0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%.

A tie-line power smoothing via a novel dynamic real-time pricing mechanism in MMGs

In a Multi-Microgrid (MMG), the microgrids (MGs) are generally scheduled individually. Uncoordinated operations in the MMG may result in tie-line power congestion, especially in peak-load hours. Previous studies have rarely reported how to coordinate MGs operation by implementing the acquired optimal day-ahead scheduling in a competitive environment. To solve this problem, a new entity- called a microgrid aggregator has been presented to manage trading power between the MMG and the main grid. In this paper, a bi-level optimization model is proposed to control the tie-line power. On the lower level, each MG optimizes its energy trading with the main grid individually and sends the result to the upper level. On the upper level, an aggregator checks the total trading power between the MMG and the grid. In case of any violation, the aggregator activates demand response programs through a represented dynamic real-time pricing. Subsequently, a new rescheduling is implemented by the local controller (LC) in each MG to achieve maximum profit. This process is a cooperative method. Thus, the aggregated profit should be fairly distributed among the microgrids. In this paper, sharing profit between MGs is implemented based on the core concept. The performance of the proposed method is assessed by simulating various scenarios in different seasons and days, which demonstrates the effectiveness and robustness of the proposed method.