A rational pricing model for demand response programs in contemporary distribution systems

Due to development of the batteries and it's charging technologies, public incentives and growing criticism on dense air pollution, the electrical vehicles (EVs) gaining more popularity as compared to traditional fuel vehicles. In this paper, the multi-objective optimal scheduling of Electric Vehicle batteries in Battery Swapping Station (BSS) is aimed in order to optimize the number of EV batteries taken from battery stock. Further, degradation cost of batteries and electricity charging cost of batteries also considered for optimal scheduling of EV batteries in BSS. The dynamic electricity pricing model is considered to avoid new peaks of battery charging demand in BSS. EV battery swapping demand in each hour of the day is solved by using a multi-objective Shuffled Frog Leaping Algorithm (SFLA). The simulation results present the effectiveness of multi-objective optimization and dynamic pricing model.

The article deals with dynamic pricing (DP) models methodological aspects, how to model DP in different spheres of services/industries, which factors have a significant effect on the DP formation and what are the benefits of DP application in practice. This paper discusses the concept of dynamic pricing, the dynamic pricing models principles, problems, main methodological assumptions and methods in retail. Therefore the aim of this paper is to analyse the dynamic pricing models principles and problems and to represent the results of DP forming factors evaluated by AHP method.

Many of the companies started moving towards cloud computing because of its characteristics like pay as you go, easy to use, scalable, multi-tenant, secure, etc. There are many cloud providers providing cloud services over the internet. There are number of pricing policies for the user. These pricing policies can be categorised as static and dynamic pricing. This paper represents the enhancement and evaluation of the proposed dynamic resource pricing model for cloud resources based on the consumer behaviour. For model evaluation, one tail two sample t-test has been used. The results show the model will be helpful to the cloud provider for profit maximisation.

This paper studies the reference effect on dynamic pricing in revenue management for cases with limited capacity and stochastic demand. We first present a single‐period fixed pricing (FP) model in finite horizon with fixed capacity and stochastic demand, and show that there is a unique optimal solution. The model is then extended to a discrete‐time dynamic pricing (DP) model as a benchmark case. We subsequently propose a DP model with reference effect (DPR), investigate the properties of the revenue function, and present a computational scheme to compute the dynamic optimal price. Numerical experiments are conducted to exhibit how the reference effect may influence the initial price, pricing trend, price dispersion, and expected revenue, with the FP, DP, and DPR policies in the same environment of a fixed capacity facing stochastic demand. We also present, through numerical examples, a comparison between deterministic demand and stochastic demand scenarios under reference effect, and with a fixed initial price versus a variable initial price.

Performance evaluation of conventional demand response at building-group-level under different electricity pricings

Dynamic pricing (a.k.a. real-time pricing) is a method of invoking a response in demand pricing electricity at hourly (or more often) intervals. Several studies have proposed dynamic pricing models that maximize the sum of the welfares of consumers and suppliers under the condition that the supply and demand are equal. They assume that the cost functions of suppliers are convex. In practice, however, they are not convex because of the startup costs of generators. On the other hand, many studies have taken startup costs into consideration for unit commitment problems (UCPs) with a fixed demand. The Lagrange multiplier of the UCP, called convex hull pricing (CHP), minimizes the uplift payment that is disadvantageous to suppliers. However, CHP has not been used in the context of demand response. This paper presents a new dynamic pricing model based on CHP. We apply CHP approach invented for the UCP to a demand response market model, and theoretically show that the CHP is given by the Lagrange multiplier of a social welfare maximization problem whose objective function is represented as the sum of the customer's utility and supplier's profit. In addition, we solve the dual problem by using an iterative algorithm based on the subgradient method. Numerical simulations show that the prices determined by our algorithm give sufficiently small uplift payments in a realistic number of iterations.

Although cloud computing is a thriving technology trend in industry and academy, the resource renting cost is still the main obstacle for users to switch to cloud. The existing pricing models are not flexible enough for users. On-demand pricing model does not guarantee resource availability, while reserved pricing model may result in high risk of resource wasting. In this paper, we propose OMTiR: An Open Market for Trading Idle Cloud Resources, enabling users to sell their unused or underutilized resources on negotiable prices. Consequently, users, either as a resource seller or buyer, can reduce the resource renting cost. In addition, the cloud provider can increase revenue by taking arbitrage profit in the market and serving more users using the same amount of resource. A comparative study is conducted using a real world workload trace to show the advantages of the open market model over the existing price models in terms of resource utilization rate and task waiting time.

In power systems, electrical consumers can become a significant source of flexibility, by adjusting their consumption according to grid’s needs while respecting their operational constraints. Consumers’ flexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Specifically, the nonlinear auto-regressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers’ price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.

We introduce a new model of user-based dynamic pricing in which decisions occur in real time and are strongly influenced by the budget constraints of users. This model captures the fundamental operation of many electronic markets that are used for allocating resources. In particular, we focus on those used in data centers and cloud computing where pricing is often an internal mechanism used to efficiently allocate virtual machines. We study the allocative properties and dynamic stability of this pricing model under a standard framework of cloud computing systems which leads to highly degenerate systems of prices. We show that as the size of the system grows the user-based budget-constrained dynamic pricing mechanism converges to the standard Walrasian prices. However, for finite systems, the prices can be non-degenerate and the allocations unfair, with large groups of users receiving allocations significantly below their fair share. In addition, we show that improper choice of price update parameters can lead to significant instabilities in prices, which could be problematic in real cloud computing systems, by inducing system instabilities and allowing manipulations by users. We construct scaling rules for parameters that reduce these instabilities.KeywordsCloud ComputingVirtual MachineEquilibrium PriceDynamic PricePrice MechanismThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

High renewable energy penetration poses significant challenges to supply‐demand balancing in transmission networks, making transmission‐storage cooperative planning (TSCP) a crucial strategy for mitigating renewable variability. Meanwhile, the carbon reduction potential on the demand side remains insufficiently explored. Coordinating grid‐side resource planning with demand response can enhance system flexibility and support deep decarbonization. To address this, this study develops a TSCP model integrated with carbon‐aware demand response under a bi‐level robust optimization framework. The upper level performs robust TSCP under source–load uncertainty, leveraging battery and hydrogen storage to smooth renewable output fluctuations, while the lower level adjusts user‐side electricity consumption in response to price signals to reduce emissions. A dynamic carbon pricing model is proposed based on carbon emission flow analysis, integrated with locational marginal pricing to form a unified electricity‐carbon price signal that stimulates low‐carbon responsiveness on the demand side. A nested alternating optimization procedure with column‐and‐constraint generation (NAOP‐C&CG) algorithm is developed for an efficient solution. Case studies on the Northwest China HRP‐38 system show that the proposed method reduces total system cost by 48.47%, decreases the load shedding rate by 0.92%, and improves renewable energy utilization by 6.6%. Furthermore, it achieves CO 2 emission reductions of 10.82 Mt and 9.4 Mt compared to fixed and conventional ladder‐type carbon pricing schemes, respectively.

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily en-ergy consumption behaviors. It helps manage flexible de-mand response resources on peak load. However, it is insuf-ficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mecha-nism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair eval-uation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superior-ity of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

Using the differences in user attribution and profitability among different business models employed by technology‐trading platforms, a two‐stage dynamic game pricing model was constructed considering three pricing strategies. The research found that (1) in the information‐intermediary business model, technology‐trading platforms should adopt a two‐stage pricing model that first emphasizes transaction fees followed by mixed system charges. (2) In the one‐stop service business model, technology‐trading platforms can achieve higher returns by comprehensively using big data analysis to tailor mixed fees. (3) The traditional information‐intermediary platform can adopt a pricing model with mixed system charges to transition to a one‐stop service platform.

In the competitive landscape of modern manufacturing, achieving optimal profit margins necessitates a balanced integration of cost management, strategic pricing, and production efficiency. Manufacturing firms operate in a dynamic environment where fluctuating raw material prices, labour costs, and technological advancements directly impact profitability. To remain sustainable and competitive, companies must adopt systematic approaches that enhance cost efficiency, optimize pricing models, and improve overall production processes. This research paper explores various methodologies and frameworks that manufacturers can employ to maximize profitability while maintaining operational effectiveness. Cost management techniques such as activity-based costing, target costing, and lean manufacturing principles are examined to highlight their role in reducing waste and improving efficiency. By integrating these methodologies, manufacturers can better allocate resources, streamline production, and achieve economies of scale. Moreover, leveraging automation, IoT-enabled predictive maintenance, and data analytics can further enhance cost control and operational agility. Pricing strategy plays a crucial role in profit margin optimization. While traditional cost-plus pricing ensures a predictable margin, dynamic pricing models and value-based pricing provide manufacturers with flexibility to adjust prices based on demand, competition, and customer perception. This paper delves into data-driven pricing mechanisms that enable businesses to maximize revenue while maintaining market competitiveness. Furthermore, penetration and skimming pricing strategies are discussed to illustrate their applicability across different market scenarios. Production efficiency is another key factor influencing profitability. The adoption of lean manufacturing, Six Sigma methodologies, and total quality management (TQM) has proven to enhance operational performance while minimizing defects and production downtime. This study provides an in-depth analysis of how firms can optimize supply chain logistics, enhance workforce productivity, and reduce bottlenecks in manufacturing processes. By implementing smart manufacturing techniques, companies can improve throughput, enhance product quality, and achieve just-in-time production goals. This study also examines real-world case studies and empirical data to illustrate successful applications of these strategies. For instance, the integration of AI-driven analytics in a leading automotive manufacturing company resulted in a 20% reduction in operational costs and a 15% improvement in productivity. Similarly, a consumer electronics firm implemented dynamic pricing algorithms to adapt to market fluctuations, leading to a 10% increase in revenue without additional marketing expenditure. Furthermore, emerging trends such as AI-driven analytics, smart manufacturing, and supply chain digitalization are examined to highlight their potential impact on future profit optimization. The role of Industry 4.0, blockchain technology in supply chain transparency, and automation in reducing human intervention are also discussed. These innovations are transforming the traditional manufacturing landscape and setting new benchmarks for efficiency and profitability.

The platform economy evolved rapidly due to technological breakthroughs, hence new challenges emerged in terms of pricing strategies affected from different user behaviors and market demand dynamics. Traditional pricing methods are not up to this type of challenge, and it requires a model able to be more agile — in real time. The study presents “DeepPrice,” a dynamic pricing model using deep learning namely Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (DRL) to achieve the optimal platform pricing strategies in response to user behavior and market signals. The research has an experimental design for the development and testing of the DeepPrice model. The model is pre-trained by the transaction data of an important e-commerce platform for this task. CNNs learn user profiles and product properties via the encoding layer, while DRL models implement the strategy of adjusting price according to behavior actions in tensor form. Metrics such as platform revenue, user conversion rates, and customer satisfaction are used to validate the model back to the model performance. Our solution helped DeepPrice to generate incremental 20% platform revenue on average and better adjust to the market challenges. It performed better than standard pricing solutions, especially in times of high demand, and effectively personalized price-pointing for top-value customers to drive higher conversions and improve customer satisfaction. This study points to the promise of using deep learning to improve dynamic pricing in platform economies. Flexible & Scalable Solution for any IndustryDeepPrice Nevertheless, challenges regarding the computational cost of implementing personalized pricing strategies and ethical debates surrounding such strategies remain to be studied in more detail. In platform economy, the reinforcement learner has a significant potential to provide a reliable real-time pricing solution with CNN in DeepPrice to improve both profit and customer satidfaction.

Hybrid game model for electricity trading and pricing among multiple microgrids and consumers based on demand-side complex networks