Electricity Pricing Strategies Research Articles

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.

Optimal design and operation of hybrid renewable energy system for drinking water treatment

The industrial process of drinking water treatment requires intensive energy, leading to high enterprise costs and abundant carbon emissions. To ensure cost-effective and low polluting power supply for the above process, this paper proposes to deploy the grid-connected hybrid renewable system. A multi-objective nonlinear dynamic model mixed with integer variables is established for the decision makers, in which both system configurations and operations as well as benefits trade-offs from four dimensions are simultaneously considered. The ε-constraint method and system planner attitude parameters are introduced to transform the proposed model into its equivalent single objective form, which is further been solved by the LINGO software. A case study in China is given to assess the viability of the proposed model, in which optimal system configuration, economically feasible self-sufficiency ratio and optimal energy balance are obtained. Influences of electricity pricing strategies and natural resource changes on the systems are also analyzed and compared. It is found that the deployment of grid-connected hybrid wind/PV/storage system can help power users to cope with the future electricity price variation risks, with the feasible self-sufficiency ratio reaching 95%.

Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.

Techno‐economic comparative study of hybrid microgrids in eight climate zones of Iran

AbstractMany efforts have been made to increase the utilization of renewable energy resources (RESs) in Iran. This paper aimed to evaluate the techno‐economic performance of an introduced hybrid microgrid (HMG) in eight climate zones of Iran. Therefore, ten cities are selected from the eight climate conditions of Iran. An electricity pricing strategy is also implemented according to the electricity tariffs defined by the Ministry of Energy (MOE) of Iran. The proposed electricity pricing strategy is applied to the HOMER software for investigating the optimal system configuration, RES electricity generation, and the economics of each understudy city. Optimization results indicate that Urmia (in moderate and rainy climate zone) has the least net present cost (NPC) (−5839$) and levelized cost of energy (COE) (−0.0122 $/kWh), whereas Golestan (in semimoderate and rainy climate zone) has the highest NPC (4520 $) and COE (0.012 $/kWh). It is shown that the combination of photovoltaic (PV)/wind turbine (WT)/converter in the grid‐connected operation mode is the most economical configuration. Moreover, the cities with higher potentials of wind speed and solar irradiance have lower NPC and COE. It is concluded that the utilization of the battery energy storage (BES) is technically and economically infeasible for all eight climate zones, even if the stored electricity is sold to the grid. Two sensitivity analyses are conducted to the electricity feed‐in‐tariff (FiT) and solar module price, respectively. The first sensitivity analysis indicates that by increasing FiT, more contribution of RESs is seen, which leads to lower COE and NPC. Furthermore, the two cities of Urmia and Yazd have the highest NPC and COE reductions. The second sensitivity analysis studies the module price impacts on the NPC and COE of each understudy city. It is revealed that the PV module price has a considerable effect on NPC and COE. However, this effect is more significant in some cities such as Bam, where a linear relationship is seen between the module price and economic results (NPC and COE).

A Multi-Objective Coordinated Charging and Discharging Strategy for Electric Vehicles Based on Stackelberg Game

For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game is proposed. As the leader, the grid company aims to stabilize load fluctuations and formulate a reasonable electricity price strategy to guide EVs to participate in vehicle-to-grid (V2G); As followers, EV users optimize their charging plans based on electricity price information with the objective of reducing costs and obtaining good comfort. This paper uses the MOPSO algorithm to solve the proposed multi-objective Stackelberg problem, and calculates the optimization results under various preferences, which proves the effectiveness of the proposed model and method.

Driving preference analysis and electricity pricing strategy comparison for electric vehicles in smart city

With the increasing population density and relatively limited space and resources, cities are becoming more intelligent to provide adequate provision of services for the inhabitants. The utilization of the Internet-of-Things and Edge-of-Things technologies presents a significant foundation for the development of smart cities where intelligent transportation system is one of the most important applications. Due to the obvious advantages of reducing energy consumption and carbon emissions, electric vehicles are playing an increasingly important role in the intelligent transportation system. However, there is no shared framework for the interaction between electric vehicles and intelligent transportation system in smart cities. To handle this issue, this work proposes a practical framework to collect trajectory data of electric vehicles via edge devices and use a novel modified dynamic time warping method to analyze drivers’ preference. The analysis based on real data shows that a certain percentage of electric vehicle drivers have driving preference. That is, they tend to go through specific routes or locations during commuting. Furthermore, a few simulation experiments are conducted to compare the system performance between the time-of-use and load-of-use pricing strategies of the charging stations. The results demonstrate that the load-of-use pricing strategy can effectively divert the traffic flow and balance the load differences between different charging stations.

Reliability Evaluation of Power System Considering Time of Use Electricity Pricing

This paper proposes an analytical method that incorporates the time of use (TOU) strategy into the reliability evaluation of power system. The TOU electricity price strategy enables the savings of electricity cost, but its impact on power system reliability has not been thoroughly studied. The proposed method constructs a peak-flat-valley period partition model and develops an equal step length iteration algorithm to obtain the optimal period partition. A price-based demand response is modeled to calculate the hourly load using an apportionment method. Using the particle swarm optimization algorithm, a TOU-based optimization model with a penalty is constructed to find the optimal electricity price. Then, an analytical “electricity price–reliability” model is developed to evaluate the reliability of the power system considering TOU. The effectiveness of the proposed method is investigated through the case study on the Roy Billinton test system and reliability test system.

Optimal Scheduling Approach on Smart Residential Community Considering Residential Load Uncertainties

With the reformation of electric power market and the development of smart grid technology, smart residential community, a new residential demand side entity, tends to play an important role in demand response program. This paper presents a day-ahead demand response scheduling model for the novel residential community considering the impact of various residential load uncertainties. In this paper, each residential load with uncertainties is firstly modeled using Copula, and the random scenarios are generated by Monte Carlo simulation. Secondly, the residential loads are classified into different categories according to various demand response strategies, and an optimal scheduling scheme for residential loads and distributed generation is modeled. Finally, the generated scenarios are integrated into the scheduling model to form the complete optimal residential demand stochastic scheduling scheme. The presented model can reduce the cost of user’s electricity consumption and decrease the peak load, load peak-valley difference, and the energy consumption of residential load profile without bringing discomfort to the users, through which residential community can participate in demand response efficiently. Besides, this model can also provide support for the decision of electricity pricing strategies under power market development.

Electricity Pricing Game Mechanism Based on Energy Chemistry Internet of Things

With the economic and technological development of the society, the reliability and stability of traditional grid power supply have been faced with more challenges. More attention should be paid to the impact on the environment accordingly. Based on the concept of energy chemistry internet of things (EC-IoT), this paper studies the grid structure and electricity market under the background of renewable energy, and also studies the pricing mechanism of electricity according to game theory. Firstly, the characteristics and basic elements of electricity pricing were analysed deeply and the relevant concept model was established, including power grid environment model, electricity market model and power generation model. Then, in view of the characteristics of the electricity pricing problem in the EC-IoT, one power system model was established. Finally, based on the Bayesian Nash equilibrium (BNE) game, the electricity pricing strategy was proposed, by systematically introducing the process of game pricing and the solution to the equilibrium of the electricity price game.

Optimal residential community demand response scheduling in smart grid

With the reformation of electric power market and the development of smart grid technology, smart residential community, a new residential demand side entity, tends to play an important role in demand response program. This paper presents a demand response scheduling model for the novel residential community incorporating the current circumstances and the future trends of demand response programs. In this paper, smart residential loads are firstly classified into different categories according to various demand response programs. Secondly, a complete scheduling scheme is modeled based on the dispatch of residential loads and distributed generation. The presented model reduces the cost of user’s electricity consumption and decreases the peak load and peak-valley difference of residential load profile without bringing discomfort to the users, through which residential community can participate in demand response efficiently. Besides, this model can also provide support for the decision of electricity pricing strategies under power market development.

Co‐optimisation for distribution networks with multi‐microgrids based on a two‐stage optimisation model with dynamic electricity pricing

Microgrids (MG) with renewable sources and storage devices play an important role in distribution networks (DNs). MG economic load dispatch normally does not support DN, especially when they belong to different companies and share different interests. To achieve optimal operation of DN with multi-MGs, a two-stage optimisation model is established based on the dynamic electricity pricing strategy. The dynamic electricity pricing strategy gives incentive for MG to participate in the operation of DN, and it is determined by the matching degree between exchange power expected by DN and the real exchange power. Moreover, the life time characteristic of a storage device system is taken into consideration in the second stage via the rain-flow-counting method. The multi-objective particle swarm optimisation is applied to solve the two-stage optimisation problem. Besides, initial particles generated based on Latin hypercube sampling, global best position obtained based on adaptive selection, and non-uniform mutation operator are applied to improve search efficiency as well as maintain the population diversity of the algorithm. Simulation results show that the power sharing between MGs’ and DNs’ can smooth the load curve of DN as well as reduce the total operation cost of MGs.

Virtual electricity retailer for residents under single electricity pricing environment

Various residential electricity pricing strategies provide diverse methods for calculating consumption costs. Due to the existence of electricity company monopolies and single residential electricity pricing systems, residents of certain areas have no option but to accept the electricity pricing offered to them. Based on local residential electricity pricing strategies, a virtual electricity retailer (VER) mechanism is put forward. The proposed VER mechanism includes a pricing package plan (PPP), a consumption-based plan, an add-on plan, and an exclusive plan. A PPP optimization pricing model was established to maximize VER profits when taking into account income, allowances from sponsors, expenditures and customer savings. Finally, payment processes were designed under a fixed pricing system and a time-of-use pricing environment. This case study shows the impact of PPPs and the allowance and demonstrates that the model helps customers save electricity while maximizing VER profits.

UDP: Usage-Based Dynamic Pricing With Privacy Preservation for Smart Grid

Smart sensing and wireless communication technologies enable the electric power grid system to deliver electricity more efficiently through the dynamic analysis of the electricity demand and supply. The current solution is to extend the traditional static electricity pricing strategy to a time-based one where peak-time prices are defined to influence electricity usage behavior of customers. However, the time-based pricing strategy is not truly dynamic and the electricity resource cannot be optimally utilized in real time. In this paper, we propose a usage-based dynamic pricing (UDP) scheme for smart grid in a community environment, which enables the electricity price to correspond to the electricity usage in real time. In the UDP scheme, to simplify price management and reduce communication overhead, we introduce distributed community gateways as proxies of the utility company to timely respond to the price enquiries from the community customers. We consider both community-wide electricity usage and individual electricity usage as factors into price management: a customer gets higher electricity unit price if its own electricity usage becomes larger under certain conditions of the community-wide collective electricity usage. Additionally, we protect the privacy of the customers by restricting the disclosure of the individual electricity usage to the community gateways. Lastly, we provide privacy and performance analysis to demonstrate that the UDP scheme supports real-time dynamic pricing in an efficient and privacy-preserving manner.

The Application of Radiant Concrete Ceiling’s Heat Storage in Wu Han, China

In temperature and humidity independent control system, although the heat storage capability of the concrete ceiling is not big, for energy-saving building with small air-conditioning load, its potential of operating cost’ saving is not negligible. Based on an energy-saving dwelling building’s typical room in Wu Han, China, this paper dynamically analyzes the room’s load and concrete ceiling’s radiating capability, get conclusion that: according to the peak-valley electricity price and reasonable intermittent operation strategy, the temperature control system can save cost by 16.6% in summer, 27.3% in winter.

Home production and the residential electric load curve

Most recent works concerning time-of-day demands for electricity totally ignore home production. In this article we show that an understanding of the timing of household productive activities is crucial to an understanding of the residential electric load curve, and to an understanding of future responses to time-of-day, seasonal, or other marginal cost based electricity pricing strategies. We show that the timing of energy-intensive household activities can be modeled in terms of classical inventory adjustment processes. Those activities characterized by long re-order cycles are most likely to be shifted in response to time-of-day prices. Residential load curve data and data from the National Time Use Study suggest that economists have tended to underestimate the importance of water heating in affecting the load curve. Because of the relatively long re-order cycle of activities using hot water, and the possibility of timing the water heater, these activities should be quite responsive to well designed time-of-use rate structures, both in the short- and long-run.