Time-of-use Power Price Research Articles

Unit scheduling considering the flexibility of intelligent temperature control appliances under TOU power price

With the continuous increase in the proportion of electricity used by commercial and residential customers and the popularity of intelligent appliances, demand response has become an important resource in improving the flexibility of the power system operation. This study focuses on exploiting the demand-side flexibility resources in unit scheduling. First, an energy management model (EMM) is established for intelligent temperature control appliances (ITCAs) of commercial and residential customers which can optimize its electricity consumption according to power prices under the smart grid, and the calculation model on the price elasticity of demand (PED) of the ITCA is built on the EMM through the mathematical derivation, by which the calculation method of ITCAs’ flexibility is proposed, namely, the PED of the ITCA is obtain with the consideration of appliance operation characteristics and customer's electricity consumption behaviors. Subsequently, taking the time-of-use (TOU) power price into account, a flexibility calculation method for ITCAs is proposed. Then, a unit scheduling model considering the flexibility of ITCAs is established in order to explore the influence of TOU power price on the power system operation. Finally, the effectiveness of the proposed method and model is verified in the IEEE-RTS79 system and a practical case in Zhangjiakou, China, respectively. The simulation results show that the proposed model can quantify the flexibility of ITCAs driven by electricity price signals and analyze the impact of the flexible response of ITCAs induced by TOU power price on the operation of the power system.

Charging Control Scheme for Electric Vehicles Based on ConsumerPsychology

With a large number of electric vehicles connected to the grid, the orderly charging problem of electric vehicles has attracted more and more attention. The present techniques control charging load of electric vehicles mostly from the perspective of centralized control, but few reports the charging model based on consumer psychology. Based on the study of the traffic travel regularity of residents, this paper establishes consumer psychology model of the charging load influenced by the electricity price. The response of the charging load to the electricity price is described by the load shifting rate. Through the optimization of peak-valley time of use power price, the peak load shifting and load curve optimization can be achieved. Finally, testing results verify the effectiveness of the proposed method.

Hybrid Energy Ratio Allocation Algorithm in a Multi-Base-Station Collaboration System

Network densification in the 5G system causes a sharp increase in system energy consumption, a development which not only increases operating cost but also carbon emission. With the development of smart power grids, multi-source energy supply and time-of-use (TOU) power price have become effective methods for reducing system energy price (namely, total energy cost). However, the energy generation velocity of renewable energy (RE) is significantly influenced by weather factors. Thus, their energy generation entails large fluctuations, and the system energy allocation strategy involves enormous challenges. Therefore, the energy generation velocity of wind power was combined in a multi-base-station (multi-BS) collaboration system. A multi-BS collaborative energy allocation algorithm called hybrid energy ratio allocation (HERA) algorithm was proposed under RE generation uncertainty. This algorithm can balance the TOU power price of a smart power grid and the energy storage of a BS. The energy supply modes of different energy ratios were adopted following different power prices and energies stored by different BSs, which can effectively mitigate the effect of RE generation fluctuation on the energy allocation strategy of the system. Simulation results indicated that the HERA algorithm can complete the energy ratio allocation of the system and reach the lowest energy cost of BSs.

Research on the load shifting potential of on-site power plants with byproduct gasholders in steel enterprises under time-of-use power price

The steel-making industry is one of the largest power consumers in China. To decrease the power purchase as well as increase the reliability of power supply, plenty of on-site power plants (OSPP) are built in steel enterprises. Byproduct gases generated from the steel production process are the main fuels for the OSPPs. Recently, with the implementation of time-of-use (TOU) power price in China, increasing attention has been paid to the collaborative scheduling between OSPPs and gasholders. The overall electricity cost can be reduced through distributing more byproduct gases to the OSPPs during the peak-price period while distributing less byproduct gases to the OSPPs during the valley-price period. In this paper, a mixed integer linear programming (MILP) based scheduling model is built to optimise the byproduct gases distribution under TOU power price. The goal is to evaluate the load shifting potential of OSPPs in steel plants, which is seldom discussed in previous studies. Case study is implemented on two steel enterprises with different configuration of OSPPs and the optimal operation strategy is also discussed.

Short-term Scheduling of Steam Power System in Iron and Steel Industry under Time-of-use Power Price

A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use (TOU) power price was presented, with minimization of total operational cost including fuel cost, equipment maintenance cost and the charge of exchange power with main grid. The model took into account the varying nature of surplus byproduct gas flows, several practical technical constraints and the impact of TOU power price. All major types of utility equipments, involving boilers, steam turbines, combined heat and power (CHP) units, and waste heat and energy recovery generators (WHERG), were separately modeled using thermodynamic balance equations and regression method. In order to solve this complex nonlinear optimization model, a new improved particle swarm optimization (IPSO) algorithm was proposed by incorporating time-variant parameters, a self-adaptive mutation scheme and efficient constraint handling strategies. Finally, a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.

Research on Reclassification of Power Users for TOU Power Price Based on Fuzzy Clustering

Study on TOU (Time-of-Use) power price in our country almost treats power users as a unified whole, but different categories of users have the different power consumption and way in the actual condition. Therefore, they have different responses to TOU power price. According to the power load features of all kinds of users, the paper presents a solution to reclassify the users using fuzzy clustering algorithm, and provides theoretical basis for implementing categorized TOU power price. Finally comparatively perfect effect is obtained by simulation analysis, and it has great reference value to perfect TOU power price and improve load curve.

Time-of-use Price Optimizing Model And Fuzzy Solving Method

Abstract Establishing appropriate Time-Of-Use (TOU) power price can shift peak load and optimize resources in society. The historical data of prices and consumptive quantities of TOU price measures are analyzed, and the reaction curve of consumers are constructed. On the basis of this, optimization model of TOU price is established on the demand side. Because optimization of min (max) and multi-objects are present in the model, to solve it, firstly transforming the objects into the restriction condition, which will construct the new multi-objects non-linear model, and then, the most satisfactory method in the fuzzy matches is adopted to solve the multi-objects model. At last, the example shows that the method is feasible and it supplies references to the pricing mechanism of TOU power price, which is beneficial to push this measure.