A data-driven methodology for dynamic pricing and demand response in electric power networks

Community microgrids, as an emerging technology, offer resiliency in operation for smart grids. Microgrids are seeing an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can easily overwhelm such electric networks. In this work, we propose an efficient demand response (DR) scheme based on dynamic pricing to enhance the capacity of the microgrid to securely host a large number of EVs. A hierarchical two-level optimization framework is introduced to realize the DR scheme. At the upper level, the dynamic prices for the participating users in DR are optimized while at the lower level, each user optimizes its energy consumption based on the price signal from the upper level. An evolutionary algorithm and a mixed-integer linear programming model is employed to solve the upper and lower level problems, respectively. Energy scheduling problems of the users are solved in a distributed manner which adds to the scalability of the approach. The proposed DR scheme is tested on a microgrid system adopted from the IEEE European low-voltage distribution network. Numerical experiments confirm the effectiveness of the proposed DR scheme compared to the benchmark pricing policies from the literature.

The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution.

Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents

Optimization modeling for dynamic price based demand response in microgrids

El impacto de la fijación dinámica de precios en las percepciones de los clientes de restaurantes y en el sentimiento hacia los preciosResumenPropósitoEste estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.Diseño/metodología/enfoqueUtilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.HallazgosLos resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.Limitaciones/implicaciones de la investigaciónLa configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.Originalidad/valorEste estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

With the development of information technology and the construction of new power systems, it has become increasingly important for industrial users with high load demands and complex adjustable potential to actively participate in demand response. Silicon carbide (SiC) enterprises serve as a representative example of such industries, and analyzing their adjustable potential is of general relevance. Properly accounting for revenue and planning production schedules in advance to participate in demand response is crucial. This paper presents a comprehensive adjustable potential evaluation model based on data processing, prediction, and analysis, considering the production process behavior patterns of SiC enterprises. First, a process flow analysis of the SiC enterprise load is conducted to derive the corresponding load system indicators. Next, the obtained load data undergo variational modal decomposition (VMD), incorporating additional features such as the load of other equipment and weather factors. These features are then input into an optimized Long Short-Term Memory (LSTM) network via the Sparrow Search Algorithm (SSA) for the prediction of different components, thereby enhancing the accuracy of the prediction model. Finally, based on the distinct characteristics of the load and the gradual implementation of time-of-use electricity pricing policies, a comprehensive cost model is developed for optimal regulation and interruption strategies. Using the predicted data, the adjustable potential of the enterprise is analyzed. The proposed method is demonstrated through real-world data from a specific enterprise in a certain region. The comparison with different models highlights the advantages and effectiveness of the proposed approach, ultimately showcasing the results of the adjustable potential analysis.

Peak reduction in decentralised electricity systems : Markets and prices for flexible planning

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

Power delivery systems are composed of generation, transmission and distribution system networks, loads, control and management systems. In the effort of providing a clean electric power to the consumer not only the energy sources need to be reformed but also all the above components have to be considered simultaneously using Information and Communication Technology (ICT) to reduce cost, save energy, increase sustainability and reliability. In addition to the traditional energy generation, transmission and distribution networks, the future grids expected to accommodate the distributed renewable energy generation, energy storage, demand side management systems, the ability to moderate consumption through dynamic pricing and demand response load reduction signaling based on grid and market conditions. Such smart grid applications require computing infrastructures that guarantees scalability, real-time, consistency, privacy and security. This paper tries to assess the smart grid applications computing requirements, review literatures in this topic and suggest green solutions.

Matching electricity supply and demand is crucial for deploying renewable energy sources. Tools such as dynamic and market-based pricing help engage energy end-users in this effort and enable demand response. Moving away from a predominant focus on the technical aspects of demand response, in this article, we explore dynamic pricing offerings in the light of energy democracy and retailers’ views on making electricity provision less inconspicuous. To this end, we examine the market offers of 59 Finnish electricity retailers, including their customer segmentation and understanding of the barriers to broader uptake of dynamic pricing. Our findings indicate that markets are in ferment; the electricity retailers are innovating and trialling new electricity products, but there are also significant concerns over vulnerable energy end-users. We also find evidence that a move towards more dynamic pricing is not a general trend. On the contrary, electricity retailers have diverse portfolios, and an increasing number of them promote products such as 'super-flat' tariffs, in which prices are detached from both the timing and volume of consumption. Dynamic pricing and transparency in energy balancing can contribute to increased energy literacy and energy democracy. However, current market offerings are not organised, and they are not developing in ways that would be conducive to the broad utilisation of these demand response tools among domestic end-users of electricity.

In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme’s optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.

Due to the rapid adoption of renewable energy sources and the increasing complexity of residential electricity consumption, there is a growing need for intelligent, scalable, and context-aware energy management systems in smart homes. This paper presents an artificial intelligence (AI)-based model that integrates high-resolution household power forecasting with dynamic demand response (DR) simulations to optimize energy consumption, enhance grid stability, and support sustainable energy transitions. Using the publicly available UCI Smart Home Energy dataset, a seven-step framework was implemented, including data preprocessing, exploratory data analysis (EDA), feature engineering, and the development of various predictive models—linear regression (LR), random forest regressor (RFR), support vector regression (SVR), k-nearest neighbors (k-NN), and long short-term memory (LSTM) neural networks. Each model was evaluated under seven DR strategies to capture temporal dependencies and nonlinear load behaviors: Peak Clipping, Valley Filling, Load Shifting, Load Leveling, Time-of-Use (ToU) Optimization, Price-Based Control, and Behavioral DR. Model performance was assessed using the mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination ( R ²). Results revealed that the LSTM model achieved the highest accuracy, with the lowest MAE (18.95 Wh), lowest RMSE (24.83 Wh), and highest R ² (0.94) under the Price-Based DR strategy. Random forest emerged as the most effective traditional model, yielding an MAE of 25.13 Wh and R ² of 0.89, particularly for Behavioral and ToU-based DR strategies. The SVR and k-NN models provided moderate accuracy, while LR performed poorly, underscoring its limitation in modeling nonlinear and dynamic energy patterns. DR simulations indicated that Price-Based, Behavioral, and ToU Optimization strategies achieved the best alignment between household loads and grid requirements while maintaining user flexibility. Visualization tools such as heatmaps, grouped bar plots, and R ² comparisons further confirmed the superior temporal modeling capability of deep learning (DL) methods. Overall, the proposed framework offers a scalable and interpretable AI-driven platform that integrates load forecasting with DR evaluation, demonstrating that DL—particularly LSTM—can substantially enhance forecasting accuracy, enable smarter DR programs, and promote sustainable energy management in smart homes.

The study examined the effect of pricing strategies on the revenue of maize marketers in Benue State, Nigeria. A multistage and purposive sampling technique was used in selecting 400 registered maize Marketers in six Local Government Areas of the State. Primary data were collected using structured questionnaires and analyzed using descriptive and inferential statistics. The findings revealed that majority (91.7%) applied cost plus pricing strategy in marketing of maize; (87.0%) used competitive pricing strategy, while (57.1%) used value based pricing strategy. The results also showed that 56.1% adopted bundle pricing strategy; (49.4%) applied geographical pricing strategy; few marketers used dynamic pricing (16.4%), psychological pricing (6.5%), premium pricing (2.6%), penetration pricing (1.6%), and price skimming (0.5%) respectively. The findings revealed that marketers who used dynamic pricing strategy made the highest sales revenue of ₦122,484.803, marketers that used penetration pricing recorded the least sales revenue of ₦104,333.33. Evidence from the study shows that dynamic pricing strategy enhance sales revenue of maize marketers by ₦15,104.32. It was recommended that government should enhance maize marketer’s access to extension services in the state by conducting training sessions focusing on enlightening maize marketers on more profitable pricing strategies to adopt. The government should also encourage continued education among maize marketers in the state through workshops so as to enable them make informed marketing decisions.

Demand response (DR) – allowing customers to respond to reliability requests and market prices by changing electricity use from their normal consumption pattern – continues to be seen as an attractive means of demand-side management and a fundamental smart-grid improvement that links supply and demand. From October 2011 to December 2013, the Demand Response Research Center at Lawrence Berkeley National Laboratory, the New York State Energy Research and Development Authority, and partners Honeywell and Akuacom, have conducted a demonstration project enabling Automated Demand Response (Auto-DR) in large commercial buildings located in New York City using Open Automated Demand Response (OpenADR) communication protocols. In particular, this project focuses on demonstrating how the OpenADR platform, enabled by Akuacom, can automate and simplify interactions between buildings and various stakeholders in New York State and enable the automation of customers’ price response to yield bill savings under dynamic pricing. In this paper, the cost control opportunities under day-ahead hourly pricing and Auto-DR control strategies are presented for four demonstration buildings; present the breakdown of Auto-DR enablement costs; summarize the field test results and their load impact; and show potential bill savings by enabling automated price response under Consolidated Edison’s Mandatory Hourly Pricing (MHP) tariff. For one of the sites, the potential bill savings at the site’s current retail rate are shown. Facility managers were given granular equipment-level opt-out capability to ensure full control of the sites during the Auto-DR implementation. The expected bill savings ranged from 1.1% to 8.0% of the total MHP bill. The automation and enablement costs ranged from $70 to $725 per kW shed. The results show that OpenADR can facilitate the automation of price response, deliver savings to the customers and opt-out capability of the implementation retains control of the sites by facility managers.

The balancing approach of electricity generation and consumption is an essential part of a reliable power system. The mismatch between supply and demand may also cause fluctuation in the power system. This study proposes an incentive and penalty-based demand response (I&P-DR) program for improving the profitability of both LSE and customers. First, we use a parameter that weighs the financial benefits of LSE and customers and provides considerable economic benefits to both sides. Second, an incentive and penalty (I&P) price scheme have been employed to recompense and penalize customers and reduce the electricity demand at peak hours. Finally, the study analyzes the importance of (I&P-DR) and its impact on customers’ sensitivity during peak intervals. Simulation results showed that the flexibility to consumption can be brought through the application of the proposed (I&P-DR) program and also provide the financial benefits to both, customers and LSEs.