TOU Price Research Articles

Optimal Dispatching of Microgrids with Development of Prosumers Sharing Energy Storage

The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.

Study on optimal energy dispatch of electricity-heat-cool-gas agricultural industrial Park with renewable energy

Since the release of China’s rural revitalization strategy, rural areas have experienced rapid development, but at the same time, the imbalance between rural energy demand and infrastructure has become more prominent. In this regard, this paper considers the use of abundant renewable energy in rural areas to participate in energy generation and build an integrated energy system for agricultural parks containing electricity, heat, cold and gas loads. Based on this idea, it is necessary to establish mathematical models for various equipment in agricultural parks. Then, the maximum output and daily load curves of the agricultural park were predicted, and the TOU price was added at the same time. Finally, the objective function is to minimize the cost of energy use, and the integrated energy system of the electricity-heat-cooling-gas agricultural park using renewable energy was optimized a few days ago. In addition, through simulation examples, it has been proven that this method can increase the utilization of renewable energy, improve energy utilization efficiency, and have dual economic and environmental benefits.

Optimal Allocation of Distributed Generators in Active Distribution Network considering TOU Price

As the distributed generators (DGs) are connected to active distribution network (ADN), it strengthens the communication interest between the customs and DGs and can facilitate energy integration. This article proposes hybrid pricing for DG configuration by taking advantage of fixed pricing and dynamic pricing. The time sequence scenario of wind-photovoltaic-load power can be got by k-means, which can balance the calculation burden with multiple scenarios. The planning model is built as the optimal objective for minimal investment in operation and maintenance of DGs, reduction of network loss, and decrease of voltage deviation. An improved simulated annealing particle swarm optimization algorithm is also proposed by refining the initialized population based on the niche fitness, introducing inertia weight with chaotic disturbance and accelerating local search with learning factor of dynamic parameter. The effectiveness and rationality of the proposed methodology are verified by simulation in the IEEE 69-bus system.

Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms

Revolutions in human activities and lifestyles result in a transition from conventional to intelligent residential building infrastructure. Conventional heating, ventilation, and air conditioning (HVAC), refrigerator and lighting system challenges are addressed without taking into account building heat gains, outdoor illuminance, and temperature. Based on these parameters, a mathematical model for cost estimation of residential building energy consumption, considering indoor heat gains, outdoor temperature, outdoor illuminance, and TOU price has been developed. A total of 46 swarm intelligence based optimization algorithms are used to optimize different building parameters. These swarm intelligence algorithms (SIA) are compared using the convergence curves, statistical and box-plot analysis and the Bald Eagle search (BES) algorithm is found to be the best algorithm among all 46 SIAs. The mean energy consumption costs of the best algorithm, BES, and the worst algorithm, fireworks algorithm (FA) are found to be Rs. 8.85 and Rs. 12.98, respectively. In addition, economic analysis has been conducted for the proposed study and it is compared with the existing models with building energy management systems (BEMS) and conventional model (without BEMS). It is observed that, based on this analysis, the cost savings achieved by the proposed study are nearer to 34% and 57% as compared to existing and conventional models.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

A Game Optimization Scheduling Strategy of Active Distribution Network With Multi-Microgrid Sharing Energy Storage

In a multi-microgrid grid-connected system, a MGCO is formed to participate in the optimization scheduling of the ADN by sharing ES, which can promote the efficient utilization of resources and obtain win–win interests for all participants. According to the complementary characteristics of ES behaviors and energy production–consumption of MGs, a game scheduling model of the ADN with the MGCO for sharing ES is established to obtain energy reciprocity and balance the interest of both parties. The ADN formulates the ToU price policy to maximize the operational benefits, and the MGCO responds to the price to obtain each member’s energy-dispatching strategy for minimizing the total operating costs. Furthermore, all members in the MGCO distribute the cooperation surplus based on the Shapley value method. The example results show that the proposed game model can balance the benefits between the ADN and multi-microgrid with sharing ES and maximize the mutual benefits of the MGCO through energy reciprocity.

Integrated scheduling of hot rolling production planning and power demand response considering order constraints and TOU price

When participating in demand response, the industrial adjustable load has the advantages of large capacity, high benefit, and easy management compared with commercial and residential loads. However, the stage of the production process is strongly coupled with the power consumption process, especially in the steel industry. Here, a multi-objective optimization model for the optimal scheduling of hot rolling load considering the actual production operation conditions was proposed, the model was abstracted into a vehicle routing problem (VRP) ,which is a typical combinatorial optimization problem .To minimize the cost of electricity and the risk of delivery order default, this paper considered the jump penalty value between adjacent slabs simultaneously, constructed an integrated scheduling model of hot rolling shop scheduling and power demand response, and designed a multi-objective production scheduling algorithm based on NSGA-II to solve the problem. The results showed that the proposed method can realize the reasonable distribution of production load under the constraints of electricity price and production.

Distribution system planning considering peak shaving of energy station

Energy stations (ESs) connected in a distribution system (DS) may lead to great impacts on the planning scheme of DS. In this context, this paper carries out a long-term DS planning model considering the peak shaving of ES, which is achieved by scheduling the input energy of ES. By regarding DS and ES as different stakeholders, a decentralized framework is devised to shave the electric peak loads in the DS planning, where the coupling relationship between the TOU price and exchanged power (e.g., the input power of ES) is clearly expressed. Specifically, an explicit adjustment formula is developed to represent the coupling relationship based on the concept of elasticity. In addition, an easily reformulated solution method is developed to address the probability distribution (PD) uncertainty of electric and cooling loads in the uncertainty-moment-based distributionally robust optimization (DRO) planning model. The chance-constrained power balance is expressed in a second order conic (SOC) format based on the conditional value at risk (CVaR) method and duality theory. Then, the SOC constraints are linearized according to the polyhedral linearization method. Furthermore, the bilinear terms of the planning model are respectively linearized by the McCormick and big-M methods. Finally, the proposed planning model is tested on a modified IEEE 33-node DS with an ES and a practical 99-node DS with an ES. Numerical results show that the proposed planning model is effective in managing PD uncertainties of loads as well as reducing costs of DS.

Day-ahead Scheduling of Islanding Microgrids Based on Wind Power Forecast

Wind power, as the main energy source of the island microgrid, has uncertain output. Reasonable setting of TOU price can improve adaptability to changes in wind power output. Based on the time series characteristics of wind speed, the EWMA is used to predict the wind speed day-ahead. Then through the method of distribution superposition fitting and roulette, the day-ahead forecast data of wind power considering power fitting error is obtained. As a reference, a new TOU price division method based on wind power output is proposed. At the same time, P2G and energy storage devices were introduced into the cogeneration system to achieve Gas-electric transformation functions. Combined with the heating and electric demand response of the user side, comprehensively considering the mutual conversion relationship between multiple energies. On the premise of considering economy and environmental protection, the Gurobi solver is called by Yalmip to get the start-stop status of the powerside unit and the user-side unit.

Research on joint optimal scheduling of air conditioning load and electric vehicle under demand response

As an important part of energy Internet carrier, demand side resources can participate in many interactions with power grid. In order to reduce the peak to valley load difference of power grid, from the perspective of tapping the combined peak shaving potential of air conditioning load and electric vehicles, guided by TOU price and direct load control, this paper proposes an optimal scheduling model with the minimum load difference and the maximum total revenue of users as the objective function. The results show that the joint optimal scheduling strategy can reduce the peak load and eliminate the “secondary peak load” caused by disorderly charging of electric vehicles.

Research on Capacity Configuration and Optimal Operation of Microgrid Energy Storage under Demand Management

In order to reduce the energy consumption cost, considering the influence of the correlation of photovoltaic output, load demand and peak valley TOU price in different periods on the optical storage capacity configuration, the system operation strategy and capacity configuration principle are determined based on the peak valley TOU price and energy storage charge state information and the actual size of photovoltaic output and load.Integrated with the historical load, time of use electricity price, state of charge, charge discharge power and other constraints of the energy storage system, the minimum objective function is utilized to optimize the output power of energy storage.The effectiveness of the model is verified by an example. The sensitivity analysis shows that different demand tariff rules affect the user demand declaration strategy. The energy storage system planning selects the light storage combination with appropriate capacity according to the demand tariff rules and the change of energy storage investment cost, which has practical engineering value.

Intelligent Regulation on Demand Response for Electric Vehicle Charging: A Dynamic Game Method

It has become an urgent problem to be solved that how to make electric vehicle (EV) give full play to shift the power grid peak load through the regulation of demand response (DR). Game theory is often used to provide a new solution for the optimal decision-making among multi-stakeholders. As a dynamic game, differential game can describe the dynamic changes of time-sharing electricity price (TOU price) about power grid and charging power about EV in real time. Considering the problem of “peak on top of peak” caused by a large number of electric vehicles' disordered charging, this paper makes the dispatching strategy of EVs entering the grid based on the TOU price, and establishes a dynamic differential game model for the power grid and EV decision makers. The model is solved by taking the TOU price of power grid and the charging power of EV as the strategy, and smoothing the peak valley difference of power grid and minimizes the charging cost of EV as the goal. In the end, DR for optimizing power grid load and reducing user's low cost is adopted to simulate the proposed model. The simulation results show that the peak-valley difference rate of the optimized power grid is reduced by 6.93%, and the cost of EV users is reduced by 71.52%. The simulation results verify the peak load regulation effect and the economic benefit of the differential game model on the power grid side.

Study on the Matrix Model of Electricity and Electricity Price

Electric vehicles change the charging time of electric vehicles according to the change of electricity price, so that the electric vehicle can be transferred as a load. The solution of electricity price is different, and its essence is based on definition. The change of electricity price has changed the influence of consumers on electricity demand. It is necessary to study and formulate TOU price according to the actual situation. In this paper, the solution of the power tariff matrix is deeply analyzed, which provides a theoretical basis for the study of TOU price and has practical significance.

Performance of solar PV micro-grid systems: A comparison study

Two solar PV micro-grid systems were stablished in this paper to examine and investigate their operation ability according to TOU price. Battery storage was used to adjust the operation strategies and bring in different economic benefits. Heat storage and simulation loads were also chosen to simulate the load variation. The power generation of the PV was comprehensively compared in two locations with different solar resources. The power output from PV systems in Ningbo is higher than that in Sichuan under local climate conditions, the accumulated power generation in Ningbo is 40% more than the power output in Sichuan. A preliminary operation strategy shows that the micro-grid can be continuously operated in the set mode. The battery storage has the capacity to achieve the economic optimization of the micro-grid systems.

Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer

<span>Load profile for residential users is different from commercial users where peak load occurs outside of work hours compared to working hours. Consequently, the Time of Use-based electricity price must be different not only in terms of price, but also in terms of time block structure. This paper examines the impacts of different TOU structures on TOU prices and load profiles of residential consumer. Four TOU structures are tested on the real load profile for a selected residential consumer area in Malaysia. Two elasticity factors are used for each structure to represent two different groups of users, a group that responds highly to price changes and a group that does not. The TOU price set for each structure is determined optimally subject to the following constraints; the price difference between the TOU and a fixed price per hour should be minimized and the amount of difference between price increase and price drop should be equal. From the analysis, the TOU structure with 12-time blocks provides better price signals and peak load reduction.</span>

Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response

The optimized operation of a building energy management system (BEMS) is of great significance to its operation security, economy, and efficiency. This paper proposes a day-ahead multiobjective optimization model for the BEMS under time-of-use price-based demand response (DR), which integrates building integrated photovoltaic with other generations to optimize the economy and occupants’ comfort by the synergetic dispatch of source–load–storage. The occupants' comfort contains three aspects of the indoor environment: visual comfort; thermal comfort; and indoor air quality comfort. With the consideration of controllable load that could participate in DR programs, the balances among different energy styles, electric, thermal, and cooling loads are guaranteed during the optimized operation. YALMIP toolbox in MATLAB was applied to solve the optimization problem. Finally, a case study was conducted to validate the effectiveness and adaptability of the proposed model.

Bi-level Optimization Method of Air-conditioning System Based on Office Building Energy Storage Characteristics

The air-conditioning system of office building is a large power consumption terminal equipment, whose unreasonable operation mode leads to low energy efficiency. Realizing the optimization of the air-conditioning system has become one of the important research contents of the electric power demand response. In this paper, in order to save electricity cost and improve energy efficiency, bi-level optimization method of air-conditioning system based on TOU price is put forward by using the energy storage characteristics of the office building itself. In the upper level, the operation mode of the air-conditioning system is optimized in order to minimize the uses’ electricity cost in the premise of ensuring user’ comfort according to the information of outdoor temperature and TOU price, and the cooling load of the air-conditioning is output to the lower level; In the lower level, the distribution mode of cooling load among the multi chillers is optimized in order to maximize the energy efficiency according to the characteristics of each chiller. Finally, the experimental results under different modes demonstrate that the strategy can improve the energy efficiency of chillers and save the electricity cost for users.

The evolution model of electricity market on the stable development in China and its dynamic analysis

According to the causal relationships between electricity consumption, electricity supply and electricity price, the complex interrelationships among all the factors in electricity market are clarified, and a net structure that every element can conduct mutually are founded. Furthermore, a novel dynamic system model of the electricity supply-consumption-price is proposed. By means of Lyapunov exponents and bifurcation diagrams, the dynamic behaviors of the system are analyzed, and an electricity market attractor is achieved. Then the model is applied to China's electricity market. With the statistical data of China between 1997 and 2012, the parameters for establishing a China-specific dynamic system of electricity market are identified by artificial neural network of machine learning. Owing to the actual system in China's electricity market is unstable, four efficient single regulatory strategies have been found by means of bifurcation diagrams. And comparative scenario analysis is employed based on the influences to electricity market and electricity balance index under all kinds of regulatory strength and regulatory strategies. The results show that the current actual situation in China's electricity market should be considered, and excessive regulation should be avoided. At the same time, it is found that the integrated regulatory strategies are better than the single regulatory strategies, and the optimal regulatory strategy of the integrated regulatory strategies is strategy-2,3,4. That is, to reform the regulation of China's current electricity market, the best way is to deepen market-oriented reforms in the electricity generation side, implementing peak-valley TOU price and adopting appropriate administrative measures.

An Intelligent Household Electricity Load Control Method Based on Demand Response

This paper presents a method for smart house electricity load control. The method, combined with TOU price and Real-time pricing, arranges various appliances and meets daily household electricity demand at the same time, so that to reduce the daily electricity consumption and realize Demand Response. First, this paper attempts to summarize problem witch need to be solved for realizing load control in a smart house. Second, the smart house load control problem was described as high-dimensional complex functions unconstrained optimization model and solved with Particle Swarm Optimization. Finally, design experiments used the method for a smart house. Experimental results show that the method can arrange various appliances and reduce electricity consumption.

Potential Analysis of Demand Response Based on Peak and Valley Time Price

Tou price is based on the price as the guidance, guiding power users according to their own electricity utilization way of flexibility and interest adjustment behavior to realize the purpose of peak peel and improve the load rate. This paper is on the basis of considering the user satisfaction, using plastic load capacity and power generation side coal saving benefit after two measures of implementing peak valley price demand response potential quantitative analysis, and feasibility of implementing peak valley price for a specific region. At last, a big industrial user in a certain area as an example for analysis, the result shows that the implementation of peak valley price can bring potential benefits, and provide a guide that the feasibility of implementing peak valley price in the region.