Cold Thermal Storage and Peak Load Reduction for Office Buildings in Saudi Arabia

This paper the chilled water and involves the investigations of ice cold thermal storage technologies along with the associated operating strategies for the air conditioning （AC） systems of the typical office buildings in Saudi Arabia, so as to reduce the electricity energy consumption during the peak load periods. In Saudi Arabia, the extensive use of AC for indoor cooling in offices composes a large proportion of the annual peak electricity demand. The very high temperatures over long summer periods, extending tYom May to October, and the low cost of energy are the key factors in the wide and extensive use of air conditioners in the kingdom. This intense cooling load adds up to the requirement increase in the capacity of power plants, which makes them under utilized during the oil：peak periods. Thermal energy storage techniques are one of the effective demand-side energy management methods. Systems with cold storage shifts all or part of the electricity requirement from peak hours to off-peak hours to reduce demand charges and/or take advantage of off-peak rates. The investigations reveal that the cold thermal energy storage techniques are effective from both technical and economic perspectives in the reduction of energy consumption in the buildings during peak periods.

Energy-Efficient Operation of Water Systems through Optimization of Load Power Reduction in Electricity Markets

Demand response(DR) is gaining more and more importance in the architecture of power systems in a context of flexible loads and high share of intermittent generation. Changes in electricity markets regulation in several countries have recently enabled an effective integration of DR mechanisms in power systems. Through its flexible components(pumps, tanks), drinking water systems are suitable candidates for energy-efficient DR mechanisms. However, these systems are often managed independently of power system operation for both economic and operational reasons. Indeed, a sufficient level of economic viability and water demands risk management are necessary for water utilities to integrate their flexibilities to power system operation. In this paper,we proposed a mathematical model for optimizing pump schedules in water systems while trading DR blocs in a spot power market during peak times. Uncertainties about water demands were considered in the mathematical model allowing to propose power reductions covering the potential risk of real-time water demand forecasting inaccuracy.Numerical results were discussed on a real water system in France, demonstrating both economic and ecological benefits.

Management of Peak Loads in an Emerging Electricity Market

An electricity market is a trading platform provided by the actors in the electricity sector to sell and buy electricity while maintaining the stability of the transmission network and minimizing energy losses.The management of electrical energy for rational use consists of all the operations that the consumers can carry out in order to minimize their electricity bill,while the producers optimize their benefits and the transmission infrastructure.The reduction of active and reactive power consumption and the smoothing of daily and yearly load profiles are the main objectives in this work.Many developed countries already have properly functioning electricity markets,but developing countries are still in their infancy of deregulated electricity markets.The major tools used in smoothing the load profiles include decentralized generation,energy storage and demand response.A load power smoothing control strategy is proposed to smooth the load power fluctuations of the distribution network.The required power change is determined by evaluating the power fluctuation rate of the load,and then the required power change is allocated to some generators or to some stored reserves.Otherwise,the consumers are made to curtail their power consumption.The ideas proposed in this work provide important opportunities for energy policy makers and regulators.These ideas would only be feasible if there exists real-time communication among the actors in the electricity market.The results indicate that as much as 1100 Megawatt-hours of energy can be stored for smoothing the load profile,when applied to the Southern Interconnected Grid of the Cameroon power system;and that Time of Use(TOU)pricing could be used instead of rotating blackouts in case of energy shortage.

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

In this paper,the installation of energy storage systems(EES)and their role in grid peak load shaving in two echelons,their distribution and generation are investigated.First,the optimal placement and capacity of the energy storage is taken into consideration,then,the charge-discharge strategy for this equipment is determined.Here,Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)are used to calculate the minimum and maximum load in the network with the presence of energy storage systems.The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach.Ultimately,the battery charge-discharge is managed at any time during the day,considering the load consumption at each hour.The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes.This shows the significance of such matters in terms of economy and technicality.

Strategy of constructing virtual peaking unit by public buildings’central air conditioning loads for day-ahead power dispatching

With the gradually widely usage of the air conditioning(AC) loads in developing countries, the urban power grid load has swiftly increased over the past decade.Especially in China, the AC load has accounted for over30% of the maximum load in many cities during summer.This paper proposes a scheme of constructing a virtual peaking unit(VPU) by public buildings’ cool storage central AC(CSCAC) systems and non-CSCAC(NCSCAC)systems for the day-ahead power network dispatching(DAPND). Considering the accumulation effect of different meteorological parameters, a short term load forecasting method of public building’s central AC(CAC) baseline load is firstly discussed. Then, a second-order equivalent thermal parameters model is established for the public building’s CAC load. Moreover, the novel load reduction control strategies for the public building’s CSCAC system and the public building’s NCSCAC system are respectively presented. Furthermore, based on the multiple-rank control strategy, the model of the DAPND with the participation of a VPU is set up. The VPU is composed of large-scale regulated public building’s CAC loads. To demonstrate the effectiveness of the proposed strategy, results of a sample study on a region in Nanjing which involves 22 public buildings’ CAC loads are described in this paper. Simulated results show that, by adopting the proposed DAPND scheme, the power network peak load in the region obviously decreases with a small enough deviation between the regulated load value and the dispatching instruction of the VPU. The total electricity-saving amount accounts for7.78% of total electricity consumption of the VPU before regulation.

Effect of Interfacial Strength on Bending Properties of Reinforced Concrete Beams

By taking into consideration of meso-scopic level, four-point bending numerical model of different interfaces was established to analyze the effect of interracial strength on the bending properties of reinforced concrete beams with the diagrams of crack pattern, the load- step curve and the cumulative AE- loading step curve. The experimental result shows that the peak load, the cracking load and the stiffness before cracking increase with the interfacial strength. Furthermore, the specimen with strong interface presents high brittleness during the failure process, while both bearing capability and ductility could be found in the specimen with moderate interfacial strength.

Multifunction Battery Energy Storage System for Distribution Networks

Battery Energy Storage System(BESS)is one of the potential solutions to increase energy system flexibility,as BESS is well suited to solve many challenges in transmission and distribution networks.Examples of distribution network’s challenges,which affect network performance,are:(i)Load disconnection or technical constraints violation,which may happen during reconfiguration after fault,(ii)Unpredictable power generation change due to Photovoltaic(PV)penetration,(iii)Undesirable PV reverse power,and(iv)Low Load Factor(LF)which may affect electricity price.In this paper,the BESS is used to support distribution networks in reconfiguration after a fault,increasing Photovoltaic(PV)penetration,cutting peak load,and loading valley filling.The paper presents a methodology for BESS optimal locations and sizing considering technical constraints during reconfiguration after a fault and PV power generation changes.For determining themaximumpower generation change due to PV,actual power registration of connected PV plants in South Cairo Electricity Distribution Company(SCEDC)was considered for a year.In addition,the paper provides a procedure for distribution network operator to employ the proposed BESS to perform multi functions such as:the ability to absorb PV power surplus,cut peak load and fill load valley for improving network’s performances.The methodology is applied to a modified IEEE 37-node and a real network part consisting of 158 nodes in SCEDC zone.The simulation studies are performed using the DIgSILENT PowerFactory software andDPL programming language.The Mixed Integer Linear Programming optimization technique(MILP)in MATLAB is employed to choose the best locations and sizing of BESS.

Modeling of a Photovoltaic Module Considering the Solar Energy Available from Horizontal Surfaces over Kuwait Area

The paper identifies and analyzes the geographical and temporal variability of solar energy in Kuwait. The fundamental solar trigonometric model has been modified to estimate daily and hourly solar radiation on horizontal surfaces on the basis of the more readily available meteorological data. The results demonstrate that Kuwait has an abundance of solar energy capability. An overview of the production and consumption of electrical energy, installed capacity, and peak loads in Kuwait is also presented. Finally, it is shown how the power produced from the photovoitaic （PV） cells depends on the solar radiation. The proposed PV module is made up of a combination of series and parallel cells to increase power, while the IoV characteristic and output power of the module each month may be obtained from the model.

Distributed Air-Conditioning Energy Management System within the Smart Grid Context

-Air-conditioning （AC） systems are the major energy consumption units in residential and commercial buildings. In the context of smart grid, optimizing AC operations leads to substantial saving in energy consumption, reducing the consumer＇s bill and contributing to the environment by minimizing carbon emissions from generating stations. This paper presents a distributed AC energy management system for buildings by using networked master-slaves controller architecture. The proposed system was designed, simulated, and experimentally tested by using real AC units in a students＇ residence hall. Based on the students＇ class schedules, several operational scenarios were implemented and tested. The proposed system implementation leads to a 40% to 60% saving of the consumed energy by the tested units. The same energy management scheme can be applied and implemented in other commercial and residential buildings.

Evaluation of Thermal Comfort and Energy Usage for an Enclosed Cavity Indifferent Climatic Zones of India

The utilization of energy in building sectors comprises 30–40%of the entire global energy consumption.Most of the energy is being utilized for cooling&heating the buildings.These cooling and heating depend on the nature of climate for different places.In this,the detailed analysis of the building envelope across five areas(viz.Srinagar,Jaisalmer,New Delhi,Thiruvananthapuram and Bangalore)representing different climatic zones had been carried out.Simulations are performed for these locations using eQUEST and ANSYS software.Three of the result output from the eQUEST simulation are used to assess the different cases.These outputs are:total electrical energy consumption of the year,peak cooling load&peak heating load for a particular time of the year.Temperature variations and airflow for a fan with moisture influence are also simulated for five different locations by using the radiation model and shear stress transport in ANSYS.The internal temperature distribution in accordance with an occupant is also compared and discussed for the five regions.Lastly,the predicted mean vote and predicted percentage of dissatisfaction are found to be in the range of−0.50 to−0.99 for all zones that signifies 13%to 30%of people are dissatisfied with the indoor environment.

Demand response:a strategy to address residential air-conditioning peak load in Australia

Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year.Residential airconditioning is widely believed to be the prime cause of the rise in peak demand but,in the absence of detailed residential demand research,there is no bottom-up empirical evidence to support this supposition or to estimate its impact.This paper first examines the developments in network peak demand,at a national,network distribution,and local distribution feeder level to show recent trends in peak demand.Secondly,this paper applies analytics to the half-hourly consumption data of a sample of Ausgrid’s interval metered customers,combined with local weather data,to develop an algorithm which can recognize air-conditioner use and can identify consumption patterns and peak load.This estimate is then compared to system peaks to determine residential airconditioning’s impact on overall demand.Finally,this paper considers the future impacts of air-conditioning load on peak demand as penetration rates reaches saturation levels and new minimum energy performance standards take effect reducing new units peak impacts.

Optimal Dispatch for Battery Energy Storage Station in Distribution Network Considering Voltage Distribution Improvement and Peak Load Shifting

Distribution networks are commonly used to demonstrate low-voltage problems.A new method to improve voltage quality is using battery energy storage stations(BESSs),which has a four-quadrant regulating capacity.In this paper,an optimal dispatching model of a distributed BESS considering peak load shifting is proposed to improve the voltage distribution in a distribution network.The objective function is to minimize the power exchange cost between the distribution network and the transmission network and the penalty cost of the voltage deviation.In the process,various constraints are considered,including the node power balance,single/two-way power flow,peak load shifting,line capacity,voltage deviation,photovoltaic station operation,main transformer capacity,and power factor of the distribution network.The big M method is used to linearize the nonlinear variables in the objective function and constraints,and the model is transformed into a mixed-integer linear programming problem,which significantly improves the model accuracy.Simulations are performed using the modified IEEE 33-node system.A typical time period is selected to analyze the node voltage variation,and the results show that the maximum voltage deviation can be reduced from 14.06%to 4.54%.The maximum peak-valley difference of the system can be reduced from 8.83 to 4.23 MW,and the voltage qualification rate can be significantly improved.Moreover,the validity of the proposed model is verified through simulations.

Interruptible Load Scheduling Model Based on an Improved Chicken Swarm Optimization Algorithm

With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.

Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern

Advancements in computational technologies for residential energy management have become widespread due to increased energy usage and peak demand in households.Controlled switching of appliances during peak periods for energy reduction is one of such solutions.However,this technique may constrict the consumer’s flexibility to utilize desired appliances to fulfil their needs.This study proposes a peak load control technique based on an end-use appliance prioritization and event detection algorithm.A noteworthy feature of the technique is the users’preferred appliance(UPA),which provides the occupants with the flexibility to use any electric load to fulfill their needs at any time whether peak demand control has been initiated or not.Furthermore,to address the challenging issue around the uncertain and varying nature of the users’energy usage pattern,an empirical bottom-up approach is adopted to characterize the consumers’diverse end-use behavior.The results obtained show good performance in terms of peak demand and energy consumption reduction,with 3%to 20%and at least 14.05%in demand reduction for time of use(ToU)periods and energy savings,respectively.These offer a further perspective on demand side management,affording load users a new cost-saving energy usage pattern.

Impact of climate change on outdoor design conditions and implications to peak loads

This study exams the impact of climate change on outdoor design conditions and peak loads of five Chinese cities over the five major climate zones for the winter and summer conditions.The design dry-bulb temperature(DDBT)and the coincident wet-bulb temperature(CWBT)for two 30-year periods;1971–2000 and 1984–2013 were analysed.It was found that the DDBT of the period 1984–2013 was higher than that of the period 1971–2000,whereas the CWBT and the corresponding outdoor enthalpy of the period 1984–2013 was lower than that of 1971–2000 at the various cumulative frequencies.This trend implies that the increment in conductive heat gain through the building envelope due to the rising temperature can be lower than the reduction in fresh air load due to the lower outdoor air enthalpy.In this case,the peak cooling loads may reduce in all five cities under study,and this is different from the widely held view that global warming will lead to more stringent outdoor design conditions,higher peak cooling loads and larger heating,ventilation and air conditioning(HVAC)plants than the current or historical status.The implications to the“free-cooling”of HVAC systems with enthalpy control are discussed as well.

Hybrid Control Strategy for Air-conditioning Loads Participating in Peak Load Reduction Through Wide-range Transport Model

This paper proposes a hybrid control strategy of air-conditioning loads(ACLs)for participating in peak load reduction.The hybrid control strategy combines the temperature setpoint adjustment(TSA)control and on/off control together to make full use of response potentials of ACLs.The primary free transport model of ACLs has been established in literature at or near a fixed temperature setpoint.In this paper,a wide-range transport(WRT)model suitable for larger value of TSA is proposed.The WRT model can be constructed easily through the parameter of devices and indoor and outdoor temperature.To modulate the aggregate response characteristics of ACLs more friendly to the power grid,the safe protocol(SP)is adopted and integrated into the WRT model,which achieves a good unification of oscillation suppression and efficient modeling.Moreover,the hybrid control strategy is implemented based on the WRT model,and the model predictive control(MPC)controller is designed considering the tracking error and control switch cost.At last,the superiority of the hybrid control strategy is verified and the performance of ACLs for peak load reduction under this controller is simulated.The simulation results show that the hybrid control strategy could exploit the load reduction potential of ACLs fully than the TSA mode and track the reference signal more accurately.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.