TOU Tariff Research Articles

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

Behind-the-meter residential photovoltaic plus battery systems – An insight to business models under net-metering

This paper contributes with an analysis of the development of photovoltaic plus battery systems under net-metering using elements of business models, from the perspective of value proposition and value capture features. The Brazilian regulatory framework for distributed generation is taken as example. Five photovoltaic plus battery schemes are envisaged and value creation is analyzed regarding two aspects: batteries can improve photovoltaic generation and load mismatch and batteries can cooperate with the distribution system. Policy implications and barriers to the implementation of these schemes are indicated. A techno-economic feasibility analysis for residential prosumers is conducted to gain insight about the viability gap in three realistic scenarios. Behind-the-meter photovoltaic plus battery systems for arbitrage under Time-of-use energy tariffs and a scenario that includes the sale of stored energy to the grid are analyzed in relation to an only-photovoltaic system scenario. Three cities with different photovoltaic generation potential, tariffs and human development index provide good diversity for the comparisons. We find that in favorable locations in terms of high TOU tariffs and good solar radiation, Behind-the-meter photovoltaic plus battery systems could be spontaneously adopted if stacking revenues are allowed, but a case-by-case study is necessary. Moreover, in other cases net present value and payback signal economic viability, then, when specific technical conditions indicate the use of batteries integrated with photovoltaic systems to mitigate negative local effects of photovoltaic generation, in many regions of the country they are viable.

Impact analysis of electricity pricing on the reliability of integrated Nepal power system

Abstract Time of Use of Electricity Pricing (TOU) strategy is adopted by many countries to reduce the peak load and minimize the cost of Electricity. In this research, the effect of the TOU tariff system on the reliability of the Integrated Nepal Power System (INPS) has been investigated. Implemented TOU is based on three time periods: Peak time, Flat time, and Valley time (PFV). The moving variable method is used to find the optimal PFV period in a day. Particle swarm optimization (PSO) is used to find optimal prices corresponding to the given periods, where two objective functions are converted to a single objective function. Prices are modeled with the demand response and a new load pattern of consumers after TOU has been obtained. Power plants of INPS are modeled into four states model and the Monte-Carlo simulation is used to find the generation adequacy indices like Loss of Load Probability (LOLP), Loss of Load Expected (LOLE), and Loss of Energy Expected (LOEE) before and after the implementation of the TOU. The risk level of INPS is reduced by 37% and peak load is reduced by 4.2% after the implementation of TOU. It is found that the reliability of INPS can be improved with the help of TOU

Optimal design of energy-flexible distributed energy systems and the impacts of energy storage specifications under evolving time-of-use tariff in cooling-dominated regions

To utilize the energy flexibility of demand side participants for achieving the global carbon-neutrality target, distributed energy systems (DESs) with active energy storages have attracted increasing attention. Existing studies mainly address the optimal configuration of DESs for enhanced energy flexibility. But the impacts of the energy storage specifications for DESs are rarely investigated, particularly under the evolving electricity markets towards carbon neutrality. This study, therefore, investigates the optimal design of energy-flexible DESs in cooling-dominated regions and the impacts of the economic and technical parameters of active energy storages under the evolving ToU tariff. A two-stage optimization method is developed for the optimal design of energy-flexible DESs. A district in Hong Kong is chosen as a reference site to perform the design case studies and impact analysis. It is found that the ToU tariff has significant impacts on the optimal design of energy-flexible DESs. The increase of the peak-to-valley ratio generally can increase the life-cycle economic benefits of DES energy-flexibility investment. The annualized life cycle cost of the optimized energy-flexible DES is decreased by up to $ 0.31 million when the peak-to-valley ratio increases from 2 to 10. A high unit price of energy storages (i.e., higher than 210 $/kWh for electric energy storages and 60 $/kWh for cold energy storages) will make them not economical to be adopted under all ToU tariffs. The maximum discharging rate of cold storages and the four specification parameters (i.e., the maximum charging rate, maximum discharging rate, charging/discharging efficiency, and lower limit of state of charge) of electric energy storages have considerable impacts on the energy-flexible DES design, and therefore are recommended to be optimized in design.

Mutual benefit analysis of price-responsive demand response program for demand-side load management through heuristic algorithm by scheduling of multi-classifier residential unit under TOU tariff regulation

Mutual benefit analysis of price-responsive demand response program for demand-side load management through heuristic algorithm by scheduling of multi-classifier residential unit under TOU tariff regulation

Hybrid-integer algorithm for a multi-objective optimal home energy management system

AbstractMost of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaining user comfort in terms of the operating waiting time of appliances. The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming, which involves constraints and consumer preferences. For optimal scheduling, the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm. Two techniques are proposed to manage the trade-off between the conflicting objectives. The first technique is the Pareto-optimal solutions classification using supervised learning methods. The second technique is called the lexicographic method. The simulations were performed based on residential building energy consumption, time-of-use pricing (TOU) and critical peak pricing (CPP). The algorithms were implemented in Python. The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio (PAR) by 28% and 49.32%, respectively, for the TOU tariff rate, and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand

Building a new power system with renewable energy as its main component is a key measure proposed by China to address the climate change problem. Strengthening demand-side management (DSM) is an important way to promote the development of a new power system. As an important economic incentive measure in DSM, the current TOU tariff is faced with the problem of a weak incentive effect due to the small tariff difference between the peak and valley periods. Against this background, a novel hybrid three-stage seasonal TOU tariff optimization model is proposed in this paper. First, the K-means++ algorithm is adopted to select the typical days of the four seasons through load curve clustering. Then, the price elasticity of the electricity demand model is constructed to calculate the self-elasticity and cross-elasticity in four seasons. Finally, the seasonal TOU tariff optimization model is constructed to determine the optimal TOU tariff. Through the proposed model, the tariff in the peak period has increased by 8.06–15.39%, and the tariff in the valley period has decreased by 18.48–27.95%. The result shows that the load in the peak period has decreased by 4.03–8.02% and the load in the valley period has increased by 6.41–9.75% through the proposed model.

Investigation of electricity load shifting under various tariff design using ant colony optimization algorithm

<span>A price-based program through a time of use tariff (TOU) program is one of the initiatives to offer sufficient benefit for both consumers and generations sides. However, without any strategy for implementing optimal load management, a new tariff design structure will lead to the miss perception by electricity consumers. Therefore, this study offers an investigation toward appropriate TOU tariff design to reflect load profiles. Concurrently, the ant colony optimization (ACO) algorithm was proposed to deal with the load shifting strategy to determine the best load profiles and reducing the consumers’ electricity cost. The sample load profiles data is obtained from various residential houses, such as single-story, double-story, semi-D, apartment, and bungalow houses. The significant comparison between baseline flat tariffs to several TOU tariffs has shown an improvement in the percentage of cost saving for approximately 7 to 40%. Furthermore, the identified load management was observed where the maximum load shifting weightage was set up to 30% to reflect the consumers’ effort towards energy efficiency (EE) program. The previously proposed TOU design was identified to be a suitable structure that can promote balancing of EE and demand response (DR) program effort in most consumers' houses category in Malaysia.</span>

Smart EV Charging Strategies Based on Charging Behavior

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

Energy cost minimization of a multifarious water heating system with energy recovery: A case of a healthcare institution

The energy usage intensity of healthcare institutions is extensive. The recorded relative typical energy consumption of hospitals has been noted to be in the range of 43 to 93 kWh/bed/day. Currently, with the global COVID 19 pandemic, the overall energy consumption of these hospitals has proportionally increased with these higher patient occupancy rates. The demand for hot water is directly proportional to occupancy rates and contributes to a large proportion of the energy consumed. Reducing the energy and associated costs of water heating processes may prove highly beneficial to the currently severely overstrained healthcare sector.In this paper the operation of a multifarious water heating system with energy recovery was optimized in order to minimize the grid energy costs based on the ToU tariff and maximum demand charges from the utility, while the required hot water temperature was maintained.A life cycle cost analysis between two baseline systems, consisting of the multifarious water heating system with and without energy recovery was compared to the optimal control approach. The results indicate that the energy recovery system with optimal control in 5.3 years. Retrospectively, the proposed initiatives may result in a potential saving of 68.23%, over a 20 year project lifetime.

Optimal day-to-day scheduling of multiple energy assets in residential buildings equipped with variable-speed heat pumps

The largest parts of energy demand within the residential sector are used for space heating (SH), domestic hot water (DHW) and space cooling (SC). The thermal and electrical needs can be mainly covered by energy systems that consist of Photovoltaics (PVs), solar thermal collectors (STCs), heat pumps (HPs) and flexibility sources, e.g. battery energy storage (BES) and thermal energy storage (TES). In this paper, various control strategies for the optimal day-to-day operation of residential energy systems that consist of a variable-speed HP, STCs, PVs, as well as BES and TES units are examined. The proposed strategies are based on the concepts of self-consumption maximization (SCM) and time-of-use arbitrage (ToUA) considering priorities on BES and TES charging. A typical Greek single-family building is assumed examining multiple criteria for the assessment of the rule-based approaches. The results demonstrate that priority on TES charging can lead to higher system performance than on BES charging. Regardless of the number of ToU tariff zones, during off/mid-peak periods the upper energy needs of the SH/SC trajectory should be fulfilled only in case of excess PV power, otherwise the cost of imported energy increases significantly. Both SCM and ToUA strategies can lead to rates of self-consumption and self-sufficiency that deviate in average per about 4% from the optimal values. Moreover, the ToUA-based strategies can approach the minimum cost of imported energy with a relative increase of 22% compared to the optimum. However, the minimum thermal discomfort due to space temperature cannot be achieved without the prediction of the daily SH/SC demand. Furthermore, the proposed strategies can reach the minimum thermal discomfort due to DHW losses, while leading to lower BES degradation rates than the optimization. Finally, from the multi-objective optimization for the first year of system operation, it is verified that the optimal dimensioning of PV, BES and TES is considerably affected by the type of rule-based strategy selected.

Optimal Energy Management Strategy for a DC Linked Hydro–PV–Wind Renewable Energy System for Hydroelectric Power Generation Optimization

The goal of this article is to create an intelligent energy management system that will control the stand-alone microgrid and power flow of a grid associated that includes Battery Energy Storage System, Fuel Cell, Wind Turbine, Diesel Generator, Photovoltaic, and a Hydro Power Plant. Storage systems are required for high dependability, while control systems are required for the system's optimum and steady functioning. The control, operation, and planning of both energy demand and production are all part of energy management. By controlling unpredictable power and providing an appropriate control algorithm for the entire system, the suggested energy management strategy is designed to handle diverse variations in power demand and supply. Under the TOU Tariff, the problem is presented as a discrete time multi-objective optimization method to minimize grid imported energy costs. It also maximizes earnings from surplus RE sales to the grid at a pre-determined RE feed-in tariff. Simulations were run using SIMULINK/MATLAB to validate and evaluate the suggested energy management approach under various power demand and power supply scenarios. The simulations indicate that the proposed energy management can fulfill demand at all times utilizing unreliable renewables like wind, solar, and hydroelectric power plants, as well as hydrogen fuel cells and batteries, without affecting load supply or power quality.

Algorithmically Developing Efficient Time-of-Use Electricity Rates

The disconnect between price variability of wholesale electricity sales and the uniformity of the retail prices observed by most consumers results in well documented inefficiencies. Time-of-use (“TOU”) electricity tariffs offer a middle-ground between dynamic wholesale prices and flat retail rates. A customer subject to a TOU rate has their consumption binned into discrete seasons (e.g., summer, winter) and periods (e.g., off-peak, mid, on-peak) – with prices varying by season and period. TOU rates can be challenging to design and the results often rely on a regulator’s subjective sense of reasonableness. In this paper, I outline a new method for developing multi-season, multi-period TOU rates using optimization techniques. This method identifies optimal TOU tariffs based on granular price and load data, using a curve-fitting approach. These TOU tariffs include explicit tradeoffs between different cost categories and between different season or period specifications. It also offers a simple method for comparing the relative efficiency of candidate rate designs, even if those designs have different numbers of seasons, periods, or price differentials. After describing the approach, I apply this methodology to a utility in California to demonstrate how it could be implemented in practice.

ToU Tariff Effect on Domestic Electricity Patterns- Australian Case Study

This article shows an evaluation of a ToU network tariff test on Australian domestic electricity customers. The test was with 444 domestic electricity customers in Tasmania, Australia. This Australian case has international implications for energy policy and regulation. Australia has the world’s highest domestic PV (photovoltaic system) adoption and combining this with high air-conditioning and water heating load leads to high diurnal variation and an emerging issue globally. Related issues include over-voltage. Thermal overload, frequency instability and voltage instability. The method was a statistical analysis of the energy use patterns using k-means clustering, and then stepwise regression to find drivers of energy reduction behaviour. There were also tests on the effect of weather and seasonal effect. The conclusions are that there was strong response from 4% of customers, and moderate response from 15%. There was a stronger response in households that were drawing more electricity and were wealthier than the average households.

Economic feasibility of battery energy storage systems for replacing peak power plants for commercial consumers under energy time of use tariffs

Abstract This work assesses the economic feasibility of replacing conventional peak power plants, such as Diesel Generator Sets (DGS), by using distributed battery energy storage systems (BESS), to implement Energy Time Shift during peak hours for commercial consumers, whose energy prices vary as a function of energy time of use (ToU tariffs). The economic analysis is performed by calculating the break-even point (BEP) for four different battery technologies: (i) lead-acid (OPzS); (ii) nickel-cadmium (NiCd); (iii) lithium-ion (Li-NCA); and (iv) Redox Flow (FeCr), using the HOMER Energy software in the simulations. One case study is analyzed for a commercial consumer connected to the MV network (Campinas/Brazil). The results show that, considering the updated 2018 BESS costs, none of the types of battery analyzed is economically attractive as a replacement for DGS. However, it can be observed that a 31%, 38% and 26% reduction in the costs of OPzS, Li-NCA and FeCr batteries, respectively, makes the installation of BESS viable. It is estimated that, in a 5- to 6-year horizon, these technologies would become economically attractive as a result of the strong decrease of the expected costs for the years to come.

Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network

The demand response program involves consumers to mitigate peak demand and reducing global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional Berhad (TNB) in Peninsular Malaysia has introduced Enhance Time of Use (ETOU) tariff. However, since 2015, small numbers join the ETOU program due to less confidence in managing their energy consumption profile. Thus, this study provides an optimum forecasting load profile model for TOU and ETOU tariffs using Artificial Neural Network (ANN). An industry's average energy profile has been used as a case study, while the forecasting technique has been conducted to find the optimum energy load profile congruently. The load shifting technique has been adopted under ETOU tariff price while integrating to the ANN procedure. A significant comparison in terms of cost reduction between TOU and ETOU electricity tariffs has been made. In contrast, ANN performance results in searching for the best-shifted load profile have been analyzed accordingly. From the proposed method, the total electricity cost saving has been founded to be saved for about 7.9% monthly. It is hoped that this work will benefit the energy authority and consumers in future action, respectively.

Integrated energy management system employing pre-emptive priority based load scheduling (PEPLS) approach at residential premises

Human work has been automated and replaced by various smart appliances at residential buildings with an objective to save time and energy. So, there is a massive upsurge of energy consumption everywhere and in due course, it resulted in increased electrical bills than before. In order to meet the growing utilization of more electricity by consumers at residences, internalization of renewable energy systems are firmly recommended for a better transformation. Hither, the IEMS is principally aiming to truncate the rate of electricity bills to consumers. Likewise, scheduling the usage of smart appliances and implementing load management practices prudently, a peak load reduction can be certainly accomplished by effectively using Solar Power, Batteries and Utility Grid. Already existing TOU tariff is employed in the process along with restructured components and renewable resources to manage the loads of different smart appliances according to their usage priority at different times of a day. This automated system is able to manage multiple heavy loads smartly with the help of renewable energy resources even at peak hours of a day.

Integrating production scheduling, maintenance planning and energy controlling for the sustainable manufacturing systems under TOU tariff

Climate change pushes the operation managers to take account of energy-saving issues in their decision-making of production scheduling and maintenance planning (PSMP). We address a PSMP problem for a single machine system under Time-of-Use electricity tariff. We consider two objectives including the makespan that measures the service level and the total energy cost that measures the energy sustainability. Both objectives are considered in a bi-objective mathematical model that is further solved using a novel heuristic algorithm consisting of two layers based on the problem decomposition. The inner layer problem, which is solved by a branch & bound algorithm, is to optimise the decision variables of preventive maintenance and machine’s setup. The outer layer problem, which is solved by a hybrid NSGA-II algorithm, is to optimise the sequence of jobs and the amount of inserted buffer time. The effectiveness and efficiency of the algorithm are demonstrated by a series of numerical experiments. The Pareto frontier can serve as a tool for managers to consider energy cost explicitly in making decisions. It is observed in some scenarios that reducing energy cost will not increase the makespan.

Optimal Balancing and Control of a Dynamic Load Demand in a Grid Connected Hybrid System using Feed–in Tariff Approach

In this paper, we have proposed, designed and simulated a grid connected residential smart home system benefiting from hybrid energy system (HES) for optimal balancing and control of a dynamic load using Feed–in Tariffs (FiT) tariffs by increasing the onsite self–consumption of solar PV system. To achieve the object function, two different PV system configuration scenarios were designed, simulated and analyzed for different case studies. In first scenario, PV system without battery backup storage system (BBSS) under FiT scheme with flat tariffs was investigated for a typical day of winter and summer. In second scenario, PV system with BBSS under FiT scheme with ToU tariffs was investigated again for a typical day of winter and summer. The optimal balancing and dynamic load control can be achieved by increasing the penetration level of solar PV by maximizing the onsite self–consumption of PV system, minimizing the grid electricity import in high ToU tariff periods, charging the battery during low ToU tariffs, discharging the battery during high ToU tariffs and exporting the excessive PV generation in peak PV generation periods especially in summer season. The simulated results suggest that significant revenue can be saved, and optimal balancing and control can be achieved when PV system is configured with BBSS under FiT scheme by utilizing ToU tariffs. The proposed will be more cost effective and optimal control can be achieved if implemented to residential community microgrids by using cooperative game theory approach.

Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes

Demand response (DR) is the response of electricity consumers to time-varying tariffs or incentives awarded by the utility. Home energy management systems are systems whose role is to control the consumption of appliances under DR programs, in a way that electricity bill is minimised. While, most researchers have done optimal scheduling only for non-interruptible appliances, in this paper, the interruptible appliances such as electric water heaters are considered. In optimal scheduling of non-interruptible appliances, the problem is commonly formulated as an optimisation problem with integer decision variables. However, consideration of interruptible appliances leads to a binary optimisation problem which is more difficult than integer optimisation problems. Since, the basic version of binary particle swarm optimisation (PSO) does not perform well in solving binary engineering optimisation problems, in this paper a new binary particle swarm optimisation with quadratic transfer function, named as quadratic binary PSO (QBPSO) is proposed for scheduling shiftable appliances in smart homes. The proposed methodology is applied for optimal scheduling in a smart home with 10 appliances, where the number of decision variables is as high as 264. Optimal scheduling is done for both RTP and TOU tariffs both with and without consideration of consumers’ comfort. The achieved results indicate the drastic effect of optimal scheduling on the reduction of electricity bill, while consumers’ comfort is not much affected. The results testify that the proposed QBPSO outperforms basic binary PSO variant and 9 other binary PSO variants with different transfer functions.