A hybrid Stackelberg–Markov framework for adaptive load scheduling and dynamic pricing in smart grids

Closed loop elastic demand control by dynamic energy pricing in smart grids

The shift to decentralized smart grids requires dynamic pricing based on demand, supported by advanced technology to adapt to behavioral changes. However, current pricing models fail to capture spatio-temporal load behavior, consumer heterogeneity, and externalities like emissions. Privacy constraints also hinder granular data collection, causing revenue loss. To address these issues, this proposal introduces the Topo-Behavioral Hybrid Learning Model (TBHLM) for dynamic pricing in smart grids. TBHLM has five key modules, ST-GNN-PNet: Uses temporal graph convolutions to forecast loads, congestion, and locational marginal prices (LMPs) with <3.5% MAPE and <3s latency. FBEM-Net: Applies federated learning for privacy-preserving elasticity modeling, achieving ~92% behavioral prediction accuracy and a 15% increase in demand response participation. MAD-RL-StackelNet: Uses multi-agent reinforcement learning for equilibrium pricing, leading to 18-22% peak shaving and a 30% rise in pricing stability. RBEIO-Opt: Integrates carbon penalties into economic dispatch, reducing emissions by 12.6% and improving welfare by 6.1%. PIDE-Engine: Uses inverse optimization for utility estimation with a privacy breach probability of <0.01%. TBHLM provides an adaptive, secure, and consumer-centric framework for real-time pricing, enhancing efficiency, sustainability, and grid intelligence sets.

Energy management has enhanced sustainability, dependability, and efficiency in smart grids. Urbanisation, technology, and consumer behaviour have boosted need for innovative power use and price control systems. The paper intends to construct ML for smart grid power use and price prediction. This work used an advanced shark smell-tuned flexible support vector machine (ASS-FSVM) to forecast smart grid price and power use. Weather stations, smart meters, and market price databases document power use and pricing. The quality and consistency of data are enhanced via the processes of cleaning and normalizing inputs. PCA reduces dimensionality by extracting pre-processed data characteristics. Optimized and tested FSVM models can anticipate smart grid power use and pricing. ASS may identify the most important dataset properties. The research evaluates electricity consumption forecasting using accuracy (98.05%), recall (98.93%), precision (97.10%), and F1-score (98.04%), and electricity price predicting using MAPE (4.32%), RMSE (5.80%), MSE (8.50%), and MAE (2.95%). The recommended strategy greatly increases forecast accuracy, helping utilities improve grid stability, demand responsiveness, and customer pricing.

Active supply–demand interactions in a smart grid are essential for reducing grid power imbalance which is important for the security and efficiency of power supply. A key element to the success of such interactions is the proper pricing strategy. The latest game-theory-based dynamic pricing methods require information exchanges not only between the supply and demand sides, but also among individual buildings, since they make decisions for one building’s demand response based on/influenced by operations of the others. However, in practical applications in which a number of buildings are considered, the latter information exchanges are extremely difficult due to the concerns of privacy, communication complexity and high computation load. Therefore, this chapter proposed a genetic algorithm-based dynamic pricing method for improving bi-directional interactions with reduced power imbalance, which does not require information exchanges among individual buildings. In this chapter, at the demand side, targeting at minimizing daily electricity cost, a non-linear programming-based demand response control is performed in individual buildings at a dynamic price given by the grid operator genetic algorithm optimizer. Targeting at reducing grid power imbalance, the genetic algorithm optimizer is used by the grid operator to search for a better dynamic price based on the aggregated demand response results. Such interaction will continue until the grid power imbalance cannot be further reduced. The impacts of demand elasticity are also investigated on performance improvements. The proposed pricing method can be used in practical applications to improve dynamic pricing of a smart grid for reduced grid power imbalance and thus increased operation efficiency.

Load shaping is one of important and challenging issues in power grid. In this paper, we propose a novel load shaping strategy based on energy storage and dynamic pricing in smart grid. In the proposed strategy, a consumer is encouraged to draw a certain amount of energy (i.e., quota) from the grid. When the actual energy demand is deviated from the quota, the consumer is faced with a higher electricity price. With the help of energy storage, the consumer can draw less electricity from the grid at a lower price by discharging energy when the demand is higher than the quota and draw more electricity from the grid at a lower price by charging energy when the demand is lower than the quota. As a result, the utility can implement load shaping and consumers can save energy cost simultaneously. Moreover, the proposed strategy can be implemented with low complexity and in a distributed fashion, which offers scalability to large number of consumers. Simulations results show the effectiveness of the proposed load shaping strategy.

Load forecasting, dynamic pricing and DSM in smart grid: A review

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay minimum possible amount along with getting maximum benefits. In here, usage based dynamic DSM pricing strategies plays their role and provide users with specific incentives. However, these reported real-time values can leak privacy of smart meter users, which can cause serious consequences such as spying. Moreover, most of dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, this paper proposes a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate differential privacy to protect the privacy of real-time smart metering data, and to calculate accurate billing we propose a noise adjustment method. Finally, we propose Demand Response enhancing Differential Pricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. The performance evaluation shows that DRDP outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.

In smart grids, dynamic pricing (e.g., time-of-use pricing (ToU), real-time pricing (RTP)) has recently attracted enormous interests from both academia and industry. Although differential pricing has been widely used in retail sectors such as broadband and mobile phone services to offer ‘right prices’ to ‘right’ customers, existing research in smart grid retail pricing mainly focus on an uniform dynamic pricing (i.e. all customers are offered at the same prices). In this paper, we take the first step towards an optimal differential pricing for smart grid retail pricing based on customer segmentation. A differential pricing framework is firstly presented which consists of customer segmentation analysis, and a two-level optimal differential pricing problem between the retailer and each customer group. At the upper level, a pricing optimization problem is formulated for the retailer while at the lower-level, an optimal tariff selection problem is formulated for each customer group (e.g., price sensitive, price insensitive) to minimize their bills. By comparing with a benchmarked uniform ToU, simulation results confirmed the feasibility and effectiveness of our proposed optimal differential pricing strategy.

A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance

The current regulatory framework for maintenance outage scheduling in distribution systems needs revision to face the challenges of future smart grids. In the smart grid context, generation units and the system operator perform new roles with different objectives, and an efficient coordination between them becomes necessary. In this paper, the distribution system operator (DSO) of a microgrid receives the proposals for short-term (ST) planned outages from the generation and transmission side, and has to decide the final outage plans, which is mandatory for the members to follow. The framework is based on a coordination procedure between the DSO and other market players. This paper undertakes the challenge of optimization problem in a smart grid where the operator faces with uncertainty. The results show the effectiveness and applicability of the proposed regulatory framework in the modified IEEE 34-bus test system.

The capabilities of the Smart Grid coupled with dynamic pricing enables the Smart Grid to adaptively manage the electricity generation and distribution. Several dynamic real-time pricing schemes have been proposed in recent times but few have been successfully implemented despite their economic and environmental benefits. In particular, the current real-time pricing schemes have not been able to incentivize subscribers to respond to time-varying prices in order for the smart-grid to fully benefit from such pricing. Traditional pricing schemes failed to incorporate fairness for end-users because real-time data gathering was expensive and impractical before smart devices were incorporated into the grid. In this paper, we propose a novel dynamic pricing model, fair dynamic pricing (FDP), to maintain reliable power supply during times of peak demand. The proposed model is analyzed and evaluated using a real-time consumer load dataset from San Diego, CA, USA. We demonstrate that in periods of peak load, the burden of generating expensive electricity is placed on subscribers responsible for creating the peaks rather than being subsidized by the remaining subscribers. Our results show that FDP improves the rates of low-demand subscribers by 18.4% and charges a penalty of up to 34% to high-demand subscribers for 400 sample observations. This percentage varies with the number of subscribers in the system during an interval and real-time prices.

Recently deployment of advanced metering and automatic load management methods make it possible to optimize energy consumption, and to release generation capacities for the purpose of providing sustainable electricity supply. The subject addressed in this paper, is proposing a practical demand response program for industrial load management in smart power grids. The main focus of the paper is modelling industrial loads and proposing a novel load scheduling algorithm to achieve an near optimal scheduling by taking into account industrial users satisfaction, dynamic electricity pricing, and constraints regarding to electricity generation capacity. An industrial plant containing 17 devices in its production line is used for simulation studies. The high convergence speed and the appropriate results are also clarified by comparing the proposed algorithm with Particle Swarm Optimization (PSO) algorithm. (4 pages)

Smart grid uses bi directional flow of information to create a distributed and efficient energy delivery network. Some of the important objectives of a smart grid include improving energy efficiency, maximizing utility, reducing cost, and controlling emission. Smart grids use demand response as an effective strategy to address this challenge. Demand response uses real time scheduling to enable customers to modify their demand according to energy consumption costs. In this paper, we consider the problem of efficient scheduling of energy consumption of users in a smart grid. Efficient energy management involves tradeoffs between the cost associated with energy consumption and a utility function. The utility function can represent the living comfort of users or gross income of the utility company. The utility function is non decreasing with respect to total utilized power. Hence, it is important to understand the tradeoffs between energy consumption and utility. The main contribution of this work is the development of a multi-objective optimization framework for efficient energy scheduling in smart grids. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is adapted for simultaneously optimizing the energy cost and utility function subject to a constraint on the power generation capacity. Simulation results show that EMOCA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.

Introduction: Smart Grids and Sustainable Energy Transformation.- PART I History, Evolution and Latest Development of Smart Grids.- Smart grids: overview of concepts, applications and latest developments.- PART II Technical Characteristics of Smart Grids.- Getting smart: the smart grid systems and the state-of-the-art technologies.- Towards a European renewable-based energy system enabled by smart grid: status and prospects.- Demand response, dynamic pricing and smart metering: enabling energy-saving and smart houses.- Microgrids, virtual power plants and our distributed energy future.-.Transmission and distribution systems of smart grids: current status and future trends.- Communication and Network Security Requirements for Smart Grid.- PART III Stakeholders in Perspective: Interests, Power, and Conflict.- Smart regulation for smart grids.- Smart grids from the consumers' perspective.- A business case for smart grid technologies: a systemic perspective.- PART IV National Case studies.- A smart transmission grid for Europe: challenges in developing grid enabling technologies.- Smart-grid technologies and progress in the USA.- Towards sustainable energy systems through collaboration between government and business: the Japanese case.- Smart grids and socio-technical systems transition: a government-led approach in Korea.- Super smart grid and low carbon economy in China.- Intelligent grid in Australia: a value proposition for distributed energy.- PART V Conclusion.- Conclusions.

Dynamic energy pricing policy introduces real‐time power‐consumption‐reflective pricing in the smart grid in order to incentivise energy consumers to schedule electricity‐consuming applications (tasks) more prudently to minimise electric bills. This has become a particularly interesting problem with the availability of photovoltaic (PV) power generation facilities and controllable energy storage systems. This study addresses the problem of concurrent task scheduling and storage management for residential energy consumers with PV and storage systems, in order to minimise the electric bill. A general type of dynamic pricing scenario is assumed where the energy price is both time‐of‐use and power dependent. Tasks are allowed to support suspend‐now and resume‐later operations. A negotiation‐based iterative approach has been proposed. In each iteration, all tasks are ripped‐up and rescheduled under a fixed storage charging/discharging scheme, and then the storage control scheme is derived based on the latest task scheduling. The concept of congestion is introduced to gradually adjust the schedule of each task, whereas dynamic programming is used to find the optimal schedule. A near‐optimal storage control algorithm is effectively implemented. Experimental results demonstrate that the proposed algorithm can achieve up to 60.95% in the total energy cost reduction compared with various baseline methods.