Real-time Pricing Model Research Articles

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

Securing demand–response in smart grids against false pricing attacks

Two-way communication systems in smart grids help to engage consumers in demand–response programs, which can bring many benefits but also make smart grids vulnerable to cyber attacks. In this paper, a cyber attack which aims to disturb the demand–response process by injecting false electricity price signals is considered. A real-time pricing model where the operator has incomplete knowledge of the private demand–response behaviors of the customers is proposed. The vulnerability of the power system to false pricing attacks is analyzed by a Markov decision process, and the dynamic interaction between the attacker and the defender is modeled as a zero-sum Markov game where neither player has full information of the game model. For the solution of the Markov game, a model-free multi-agent reinforcement learning method is proposed to find the Nash Equilibrium policies for both players. The proposed method is applied to the IEEE 34 Node Test Feeder, in which the effect of the defense on mitigating the impact of the attack is demonstrated and different policies of the players given various resources are analyzed. The results shows that the studied cyber attack can cause a maximum of 1.96% unsatisfied load and the proposed defense measure can reduce the unsatisfied load by 50%–70% compared with the cases without defense measures.

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.

Optimal scheduling of an integrated energy system considering carbon trading mechanism and multi-energy supply uncertainties in a real-time price environment

This research presents an optimized scheduling model for Integrated Energy Systems (IES) that harmonizes economic efficiency with low carbon emissions in a Real-Time Price (RTP) environment. Integrating a ladder-type carbon tax approach, this model adeptly addresses the unpredictability of renewable energy generation and IES load demands. It introduces a carbon trading mechanism for efficient emission control, complemented by an RTP model that manages interactions between the IES and the main power grid through piecewise linear functions. The model also proposes an adaptable operation strategy for thermoelectric and electric cooling ratios, enhancing the IES’s economic and operational flexibility. The objective function, crafted on a deviation preference optimization basis, reflects the diverse goals of decision-makers, and the model is skillfully translated into a mixed-integer linear programming format through a series of mathematical transformations. Simulation results demonstrate the model’s efficacy in achieving an optimal balance between cost-effectiveness and reliability, particularly by setting a strategic confidence level for spinning reserves. Additionally, incorporating a ladder-type carbon tax and Electric Vehicle (EV) charging within the IES underscores its significant environmental benefits, marking a stride towards a low-carbon future.

Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources

The integration of flexible loads, distributed energy resources, and other technologies is becoming common in advance power and energy systems. However, the integration also presents significant challenges due to the increasing complexity and uncertainty. To effectively manage these resources, an adaptive pricing mechanism is needed that can account for their variable availability. Based on this, we propose a new bilevel real-time pricing model that considers different distributed energy resources, uncertainty of renewable energy generation, carbon trading mechanisms, and grid fluctuations. Specifically, the upper-level optimization problem aims to maximize the total profit of the supplier that applies Q-learning algorithm. The lower-level optimization problem addresses the need for each user to make optimal power consumption decisions by constructing an individual Markov Decision Process framework for each user. The bilevel model achieves an effective balance of interests between the supplier and users by simultaneously considering both the upper-level and lower-level optimization problems. Additionally, our model can be efficiently solved using the distributed algorithm without the need to acquire transition probabilities. Simulation results show that the method is highly effective in balancing power supply and demand between the supplier and users, reducing carbon emissions, and mitigating power fluctuations.

PLUG: A City-Friendly Navigation Model for Electric Vehicles with Power Load Balancing upon the Grid

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model.

Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses.

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.

Smart Home Appliances’ Scheduling by Two-Stage Optimization with Real-Time Price Model

Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances’ operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems.

Real-time pricing method for smart grid based on social welfare maximization model

<p style='text-indent:20px;'>With the applications of big data, the research of real-time pricing method for smart grid has become increasingly important. Based on the demand side management and the real-time pricing model, the social welfare maximization model of smart grid is considered. We transform it by Karush-Kuhn-Tucker condition, then the social welfare maximization model is transformed into a nonsmooth equation by Fischer-Burmeister function. Then, taking advantage of simple calculation and small storage, we propose a new smoothing conjugate gradient method to solve real-time pricing problem for smart grid based on the social welfare maximization. Under general conditions, the global convergence of the new proposed method is proved. Finally, the numerical simulation results show the effectiveness of the proposed method for solving the real-time pricing problems for smart grid based on the social welfare maximization.</p>

Real-Time Electricity Retail Pricing Dual Optimization With Context-Based Fuzzy Optimal Algorithm

Despite its bright prospect to promote affordable energy, one of the main concerns of real-time retail pricing for electricity is price volatility that would create potential bill shocks especially for low-income consumers. This would demotivate consumers to participate in real-time pricing scheme or to act as responsive participants to reduce peak demand. As a motivation to address this concern, this article proposes dual optimization method with fuzzy optimal algorithm to solve the optimization problem presented in the real-time pricing model with the aims to reduce price volatility and to lower the minimum price further. The fuzzy optimal algorithm applies a context-based fuzzy inferencing and a gradient adjustment to arrive at the optimal retail price for every time interval. Context-based fuzzy inferencing allows fuzzy value redefinition so that the desired level of precision is preserved. Gradient adjustment simplifies the derivation of the optimal retail price via a series of iterations. Simulation results reveal that price fluctuations are able to be reduced which would create stability and avoid undesired price spikes. At the same time, the results confirm that further reduction in the minimum retail price can be achieved which would improve welfare benefits while maintaining the desired level of consumer satisfaction.

Real-time pricing for smart grid considering energy complementarity of a microgrid interacting with the large grid

Currently, generating electricity relies primarily on fossil energy, which is not only limited in resources but also harmful to the environment as it emits carbon dioxide and other gases. For contributing to carbon peaking and carbon neutrality, building a resource-saving and environment-friendly society, clean energy should be used for generating electricity as much as possible. Additionally, the overproduction of electricity by suppliers and the instability of power grids, which are caused by the information asymmetry between customers and power suppliers, can be solved by the smart grid based on smart meters. Motivated by the two aspects, a smart power system combining a microgrid system based on clean energy generation with a supplier based on fossil energy generation is considered. For pursuing maximal social welfare under joint power supply, a real-time pricing model is established. This model, which is convex, does not explicitly contain the electricity price variable such that the optimal Lagrange multiplier of its Karush–Kuhn–Tucker condition, i.e., shadow price, is taken as the optimal electricity price. For solving the Karush–Kuhn–Tucker system, the smooth Levenberg–Marquardt algorithm is used to solve its approximate smooth system of equations. The simulation results demonstrate that the proposed pricing model, joint supply mechanism and the smooth Levenberg–Marquardt algorithm are reasonable, feasible and effective.

Pareto Efficient Incentive-Based Real-Time Pricing Model for Balanced Smart Grids

Pareto Efficient Incentive-Based Real-Time Pricing Model for Balanced Smart Grids

Energy storage configuration and day-ahead pricing strategy for electricity retailers considering demand response profit

Real-time price(RTP) is one of the common methods for electricity retailers to adjust load demands and gain revenue from demand response(DR) programs. However, effectiveness and accuracy of the RTP directly affect the DR quantity, which reflects as revenue returns in DR programs. A method is proposed to maximize profit of the electricity retailer by configurating energy storage system(ESS) and coordinate the operation of ESS with RTP to participate in DR programs, which enriches the profitability measures of retailers. Firstly, an improved CNN-LSTM algorithm is utilized to establish the DR dynamic characteristic model with expected consumption as input and RTP as output. Thus, the demand curve during each period can be generated, from which optimal RTP with maximum profit of the retailer can be estimated. Secondly, a bi-level optimization model is established to maximize the profit from the DR program in a whole day. The upper level real-time pricing model takes maximum profit of the retailer as the objective by optimizing the RTP with fixed ESS configuration. The lower level ESS configuration model takes maximum profit of ESS as the objective with RTP fixed by the upper level model, from which the rated power, capacity and daily operation strategy of ESS can be optimized and sent to the upper level model. Finally, the optimal profit gained by the retailer can obtained by iterative calculation between the upper and lower level models. Based on the historical data from PJM market, the case demonstrates that the algorithm proposed can describe the DR dynamic characteristic effectively and accurately. Moreover, by configurating ESS, the retailer’s profit extra increases by 7.19%.

Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach

With the penetration of intermittent renewable energy sources, greater uncertainty has been brought to the power generation system, creating increased challenges to real-time pricing (RTP). Different from the existing studies, this paper aims to design an RTP strategy for the smart grid which integrates multi-energy generation on the supply side. Without loss of generality, small-scale distributed energy generation and power storage devices for users are also considered. Taking the interests of both supply and demand sides into consideration, a bilevel stochastic model for real-time demand response in the framework of Markov decision process (MDP) is formulated. The model well captures the interactive characters of both sides. Regarding the difficulty of collecting exact information from users in a centralized way in practice, a novel distributed online multi-agent reinforcement learning algorithm is proposed to solve the MDP model without acquisition of the transition probabilities. Through the information interaction between the upper and lower levels, the real-time electricity prices are decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. Simulation results demonstrate that the proposed pricing method and algorithm have a good performance in cutting peak and filling the valley and guarantee the benefits of both supply and demand.

The real-time pricing optimization model of smart grid based on the utility function of the logistic function

The utility function is very significant for solving the real-time pricing problem of smart grid. Based on the Logistic function, a new utility function is constructed to satisfy four properties of the utility function. In addition, from the perspective of social welfare, the real-time pricing optimization model of smart grid is established. By using the KKT conditions and the improved Fischer-Burmerister smoothing function, the optimization model is transformed into a smoothing equations problem and the smoothing Newton algorithm is used to obtain the optimal solution of the problem. The nonsingularity of the Jacobian matrix and the global convergence of the algorithm are proved. The simulation results show that, compared with previous quadratic and logarithmic utility functions, the new utility function can not only reduce the user’s electricity consumption and the supplier’s cost can but also improve the user’s utility and the total social welfare, which also indicates that the new utility function is effective in establishing the real-time pricing model of smart grid. Furthermore, the iteration times of several algorithms to solve the real-time pricing problem of smart grid are compared, which showed that the convergence rate of the smoothing Newton algorithm is very fast.

Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach

Unbalanced power demand across time slots causes overload in a specific time zone. Various studies have proved that this can be mitigated through smart grid and price policy, but research on time preference is insufficient. This study proposed a real-time pricing model on a smart grid through a two-stage Stackelberg game model based on a utility function that reflects the user’s time preference. In the first step, the suppliers determine the profit-maximizing price, and then, the users decide the electricity usage schedule according to the given price. Nash equilibrium and comparative analysis of the proposed game explain the relationship between time preference, price, and usage. Additionally, a Monte Carlo simulation demonstrated the effect of the change in time preference distribution. The experimental results confirmed that the proposed real-time pricing method lowers peak-to-average ratio (PAR) and increases overall social welfare. This study is meaningful in that it presents a pricing method that considers both users’ and suppliers’ strategies with time preference. It is expected that the proposed method would contribute to a reduction in the need for additional power generation facilities through efficient operation of the smart grid.

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

Since the inherent intermittency and uncertainty of renewable energy resources complicates efficient Microgrid operations, a Demand Response (DR) scheme is implemented for customers in the grid to alter their power-usage patterns. However, for a real-time pricing model at the current DR, the automated decision on the energy price is not trustworthy because of artificial interferences to the power generation. As opposed to energy price, an operational cost-based prosumer scheduling approach would be able to protect the integrity of the power grid operations from deceptive market transactions and assist in robust energy management. To investigate the operational challenges associated with the costs and prosumers in the Microgrid, we focus on formulating the problem mathematically and designing approximation algorithms to solve the problem of how to optimally identify suppliers to minimize the total operational costs associated with providing electricity. We prove the hardness of the scheduling as one of the NP-Hard problems and propose polynomial time algorithms for approximating optimal solutions. With a proper resilience level for reliable power services, the scheduling algorithms include ways to construct not only robust supplier networks, but also group energy communities in terms of black start while minimizing the operational costs. A series of theoretical performances and experimental evaluations also demonstrates the practical effectiveness of this scheduling model for the operations.

Optimal Control Strategy for a Marine Current Farm Integrated with a Hybrid PV System/Offshore Wind/Battery Energy Storage System

This paper suggests an automated control technique constructed on the Multi-Objective Particle Swarm Optimization to enhance the operation of a wind farm, a marine power plant and a photovoltaic array with a battery energy storing system. due to changes in PV / wind / tide, and to boost the efficiency of offshore wind farms and marine power stations connected to the battery-powered storage system, with a view to smoothing power production, the aim of projected automatic control strategy is to minimize power fluctuations and voltage variations. The battery energy storage network was used with an optimized demand response strategy based on the real-time pricing model to improve stability and power efficiency, reduce the power fluctuations and variations in bus voltage and address renewable energy generation instability. The multi-objective particle swarm optimization-based energy management programming model would be used to minimize running costs, pollutant emissions, increase the demand response benefits of micro grid operators and, at the same time, meet the load demand constraints from customers of all sorts, such as domestic, commercial and industrial users.