TOU Tariff Research Articles

Multi-objective optimization for reducing the auxiliary electric energy peak in low cost solar domestic hot-water heating systems in Brazil

Domestic water heating in Brazil is commonly done by electric showerheads, characterized by a large installed capacity and a low load factor. In that regard, consumers and utility companies present opposite interests, the first aim to reduce their electricity bill, while companies are interested on shaving on-peak consumption. Solar technologies offer significant potential for domestic applications, but the implementation is commonly hindered by economic issues. The present work shows a methodology for addressing the impact of promotion policies in terms of the benefits for consumers and utility companies. It is proposed a weighting procedure that allows to examine both interests in a single objective function. It enables determining a trade-off curve and optimizing the design parameters of the solar system (collector area, storage volume and set point temperature). Two policy scenarios were analyzed: a rebate program and the implementation of a time-of-use tariff (TOU) scheme. The results derived from the first policy scenario show the existence of a trade-off curve between the initial investment and the yearly electricity consumption, which is useful for addressing the impact of the monetary incentive considered for rebating the initial cost of the solar system. The second policy scenario shows a trade-off curve between the annualized life cycle cost of the system and the yearly on-peak electricity consumption. That curve can be used for assessing the influence of the TOU tariff on the on-peak electricity utilization, allowing to measure the impacts of the tariff scheme, and providing the basic parameters for negotiation strategies between company planners and relevant consumers.

An efficient city energy management system with secure routing communication using WSN

The major goal of this work is to reduce the economic consumption of power in the electricity as well as reduce the cost even in the high peak time usage of electricity. Another aspect is to maintain the information secure during the transmission between the producer and consumer. Here, need to maintain the balance on the efficient demand by reducing the cost of the electrical power. This proposed approach is developed to minimize the energy consumption, maintain the load in the peak hours and reduce the cost based on the TOU tariff. This helps to enhance the OREM scheme. Simulation results have performed using iHEM technology with the local energy presence. Another important aspect of the proposed approach helps to detect the intrusions. With the help of a modified dynamic source routing with adaptive local routing protocol routing (MDSR-ALR) protocol, misrouting paths are identified in wireless sensor network. These misrouting paths will generate a higher drop rate and that can be detected and reduced by the proposed application. This proposed WSN-based city energy management applications approach uses MDSR-ALR protocol which deals with the collection of information about neighbors, shortest path, and normal path and then guard time. Finally, this proposed protocol concentrates on secure routing as well as reduce the energy consumption and cost and then maintain the load.

Demand response optimized heat pump control for service sector buildings

With an increasing amount of volatile renewable electrical energy, the balancing of demand and supply becomes more and more demanding. Demand response is one of the emerging tools in this new landscape. Targeting service sector buildings, we investigated a tariff driven demand response model as a means to shave electrical peak loads and thus reducing grid balancing energy. In this paper is presented a software framework for load shifting which uses a tariff signal for the electric energy as minimization target. The framework can be used both on top of an existing building management system to shift heat generation towards low-tariff times, as well as to simulate load shifting for different buildings, heat pumps and storage configurations. Its modular architecture allows us to easily replace optimizers, weather data providers or building management system adapters. Our results show that even with the current TOU tariff system, up to 34 % of cost savings and up to 20 % reduction in energy consumption can be achieved. With Sub-MPC, a modified MPC optimizer, we could reduce computing times by a factor 50, while only slightly affecting the quality of the optimization.

Assessing the impact of changes in the electricity price structure on dairy farm energy costs

This study aims to provide information on the changes in electricity consumption and costs on dairy farms, through the simulation of various electricity tariffs that may exist in the future and how these tariffs interact with changes in farm management (i.e. shifting the milking operation to an earlier or later time of the day). A previously developed model capable of simulating electricity consumption and costs on dairy farms (MECD) was used to simulate five different electricity tariffs (Flat, Day&Night, Time of Use Tariff 1 (TOU1), TOU2 and Real Time Pricing (RTP)) on three representative Irish dairy farms: a small farm (SF), a medium farm (MF) and a large farm (LF). The Flat tariff consisted of one electricity price for all time periods, the Day&Night tariff consisted of two electricity prices, a high rate from 09:00 to 00:00h and a low rate thereafter. The TOU tariff structure was similar to that of the Day&Night tariff except that a peak price band was introduced between 17:00 and 19:00h. The RTP tariff varied dynamically according to the electricity demand on the national grid. The model used in these simulations was a mechanistic mathematical representation of the electricity consumption that simulated farm equipment under the following headings; milk cooling system, water heating system, milking machine system, lighting systems, water pump systems and the winter housing facilities. The effect of milking start time was simulated to determine the effect on electricity consumption and costs at farm level. The earliest AM milking start time and the latest PM milking start time resulted in the lowest energy consumption. The difference between the lowest and highest electricity consumption within a farm was 7% for SF, 5% for MF and 5% for LF. This difference was accounted for by the variation in the milk cooling system coefficient of performance. The greatest scope to reduce total annual electricity costs by adjusting milking start times was on TOU2 (39%, 34% and 33% of total annual electricity costs on the SF, MF and LF) and the least scope for reductions using this method was on the Flat tariff (7%, 5% and 7% of total annual electricity costs). The potential for reduction of annual electricity consumption and related costs per litre of milk produced by adjusting milking times was higher for the LF than the SF or MF across all electricity tariffs. It is anticipated that these results and the use of the MECD will help support the decision-making process at farm level around increasing energy efficiency and electricity cost forecasts in future electricity pricing tariff structures.

A new approach to congestion pricing in electricity markets: Improving user pays pricing incentives

Electricity pricing has traditionally been based on average cost pricing where consumers pay a ‘flat’ tariff based upon the average cost of production and transportation of electricity. The introduction of new ‘smart’ meters allows electricity providers to differentiate tariffs on the basis of time. Utilising congestion pricing theory, the energy industry has embraced ‘time-of-use’ (ToU) tariffs with a view to more efficiently pricing electricity. This paper demonstrates that pricing as a function of demand variability (reflecting capacity utilisation) is a more appropriate alternative to existing ToU tariffs for more efficiently allocating costs to end users. We call this new alternative pricing model ‘first derivative ratio’ FDR pricing. This new approach to congestion pricing could be applied to markets other than electricity, such as road transportation.

Energy Efficient Temperature Control with Particle Swarm Optimization for Smart Buildings

In this paper, the energy efficient temperature control algorithm in smart buildings is proposed using particle swarm optimization (PSO). A control horizon switching method with PSO is used for optimal control, and the TOU tariff is included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed algorithms.

냉방시스템 제어구간설정 방법을 이용한 건물 실내온도 제어

본 논문에서는 TOU 요금제 기반에서 제어구간 설정방법을 사용한 건물 냉방시스템 실내온도 제어 알고리즘을 제안한다. 건물 실내온도를 최적제어하기 위한 제어구간 설정은 스위칭방법과 선형계획법을 사용하고 냉방시스템 사용요금은 TOU와 피크 요금을 포함하여 산정한다. 시뮬레이션을 통해 제안한 제어구간 설정방법을 적용하면 전력 사용에 따른 비용의 절감과 피크 전력 절감을 확인할 수 있다. In this paper, the TOU tariff's based building indoor temperature control algorithm in cooling systems is proposed using control horizon method. A control horizon switching method and linear programming algorithm is used for optimal control, and both TOU and peak tariffs are included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed algorithms.

PLC-based Load Management in Steel Rolling Mills

ABSTRACT This article presents a physically-based load model and formulation for continuous process industries to use in implementing industrial load management. The formulation utilizes an integer programming technique for minimizing electricity costs by scheduling the loads and satisfying the process, production, and maximum demand constraints. The case study of a typical steel rolling mill, with the proposed model, shows that savings of about 5.21% in electricity costs are possible with optimal load scheduling under TOU tariff. After scheduling the loads, real time implementation of load management action is investigated using a programmable logic controller.

Optimal operation of industrial cogeneration for load management

This paper presents a generalized formulation to determine the optimal operating strategy of industrial cogeneration schemes. The model includes both electrical and thermal systems. All types of cogeneration equipment viz steam turbines, gas turbines, diesel generators, steam boilers, waste heat recovery boilers, and steam header configuration, with grid connection are separately represented in terms of their characteristics so that the model has the flexibility to be applicable for any industry. The model is multiperiod and nonlinear in nature and utilizes a Newton based algorithm for minimizing the total operating cost. Optimal operating strategies for different equipment combinations for a typical industrial configuration under different electricity tariff rates are determined using the proposed model. The results show that industrial cogeneration has a significant potential in reducing peak coincident demand. The optimal response of cogeneration plant reduces the peak coincident demand by 42.8 MW (71%) under flat tariff and 54 MW (90%) under TOU tariff. The industry gets 16% saving in the total operating cost with the optimal operation of the cogeneration plant. When power export is permitted to grid, it provides the utility a peak saving of 63.7 MW.

Optimum design and operation of ice-storage air-conditioning systems under Australian TOU tariffs

Ice-storage means the manufacture of ice by refrigeration systems during off-peak periods, to be re-melted to meet cooling requirements during peak demand periods. This paper deals with the economics of using ice-storage systems in Australian commercial buildings and with the role that the differently-shaped time-of-use electricity tariffs on offer in Sydney and Melbourne have on the optimum system design. Using mathematical programming techniques, the optimum store control strategy is investigated for different seasonal and load conditions. In both cities, the use of the ice-storage concept is shown to reduce overall air-conditioning costs significantly. The extra capital costs are paid back with savings in electricity in less than two years for most of the cases investigated.

Estimation of time-of-use pricing response in the absence of experimental data: An application of the methodology of data transferability

A random coefficients regression framework is used to characterize the problem of pooling the results of various designed experiments in time-of-use pricing for electricity. The ‘empirical Bayes’ estimation approach fostered by Lindley and Smith provides for the inclusion of prior information on the variance–covariance matrix of regression coefficients across experiments and a means to impute an estimate of price response (in this case the elasticity of substitution) to a region which did not have an experiment. The methodology is illustrated with an application where two experiments that were conducted in Southern California are pooled in order to infer a measure of price response to an example TOU tariff in a large midwestern utility.