Load Factor Improvement Research Articles

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Evaluating the impact of industrial loads on the performance of solar PV/diesel hybrid renewable energy systems for rural electrification in Ghana

Access to reliable electricity remains a significant challenge in many rural communities worldwide. Off-grid solar PV hybrid renewable energy systems (HRES) have emerged as a viable option for rural electrification. However, rural communities' lack of productive load often limits their effectiveness. This study aimed to assess the impact of agro-processing productive loads on the performance of off-grid solar PV HRES for rural electrification. Hybrid Optimization Multiple Energy Resource (HOMER) software was used to perform a techno-economic analysis of a solar PV/diesel HRES. The study findings showed improvement in the rural community's load factor and solar load correlation with the integration of the productive load. Subsequently, increasing the renewable energy fraction in solar PV/diesel HRES reduces the levelized cost of energy (LCOE), making electricity generation more cost-effective for rural electrification in Ghana. Comparatively, the improved LCOE was found to be substantially higher than the End User Tariff of all residential consumers on the national grid, even under high PV penetration and full capital cost subsidy cases. The study provides valuable insights into the role of agro-based productive loads in enhancing the performance of rural off-grid solar HRES.

Elasticity modelling of price-based demand response programs considering customer’s different behavioural patterns

Mathematical modelling of demand response programs (DRPs) aids regulators and policymakers in analysing the advantages of price-elastic loads on distribution system and electricity market. This mathematical model is used to find updated demand after DR for calculation of different technical and economical indices. In accordance with the concept of price elasticity of demand, an economic model of price sensitive load is developed. Price elasticity model (PEM) is a very useful tool to assess customer participation in DRPs. In this work PEM has been attributed on the ground of price elasticity of demand where PEM has been modelled by means of analytical and stochastic elasticity approach. Analytical elasticity has been modelled on the basis of ideal (lossless) approach whereas Stochastic elasticity has been modelled using Ornstein–Uhlenbeck process based on load flexibility and results obtained from these approaches were compared to check the acceptability of DR programs. It is observed that the proposed APEM and SPEM approaches provides maximum peak curtailment of 15% and 10.2% respectively during peak hours. Similarly, it is also noticed that both approaches improve load factor by reducing its peak-to-valley span by 47.33% and 35.7% respectively. The proposed models are investigated on standard IEEE 33-bus distribution system and modified IEEE 33-bus distribution system and are compared with already existing DR models. Technical and economical comparison of proposed approaches on both distribution systems shows that proposed models are competent enough to model customer behaviour properly.

Analyzing Peak Clipping for Load Factor Improvement: Real Case Studies of a Solar Power Plant in a University Campus

Analyzing Peak Clipping for Load Factor Improvement: Real Case Studies of a Solar Power Plant in a University Campus

Performance enhancement of smart grid with demand side management program contemplating the effect of uncertainty of renewable energy sources

ABSTRACT In a day ahead electricity market, all candidates of the electricity market, i.e. electricity users, aggregator, and grid operator urge to grow individual profit, but it is quite challenging to assure profit for all the candidates at a time. In this work, a multi-objective problem is formulated by combining the concept of demand side management (DSM) and Dynamic Economic Emission Dispatch (DEED). The multi-objective DSM – DEED problem is optimized by class topper optimization algorithm. In addition, an energy management algorithm (EMA) is proposed for optimal power utilization from various energy sources and to match the load demand with generated energy in presence of uncertainties of RESs. To get an accurate model, a random forest regression-based machine learning approach is considered in this paper to predict load demand, wind, and solar power on an hourly basis for a span of 24 hours. The objective here is to optimally schedule load consumption and power generation patterns simultaneously for a day to improve load factor, minimize operational cost, and maximize the profit of all the candidates of the electricity market simultaneously. The simulation findings highlight the effects of the proposed EMA and modified DSM program on the smart grid’s economy and performance.

Load Factor Improvement of the Electricity Grid Considering Distributed Energy Resources Operation and Regulation of Peak Load

In recent decades, in urban centers, several services (such as health, food, security, connectivity, etc.) have become more dependent on electricity. With the population increase, the energy requirements of these services can lead the electricity network (EN) to a state of operational stress. In this scenario, distribution companies (DISCOs) need to develop efficient energy management strategies to avoid wasting electricity in its EN. The load factor (LF) of an EN is an indicator related to efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers, together with the operation of distributed energy resources (DERs), such as renewable energy sources (RESs) and capacitor banks (CBs), represents a promising alternative. In order to address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DERs in the EN. Operational constraints related to the performance of EN, RESs, and CBs are considered. Uncertainties in the habitual demand profiles and solar irradiance during the day are represented through simulation algorithms. In order to evaluate the proposed model, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring an efficient EN operation.

A dynamic and proactive multi-microgrid network reconfiguration model for load factor improvement

Independent operation of a single microgrid (MG) is facing significant operational challenges such as high operational expenses, limited self-consumption of local renewable energy, and fluctuating peak and valley load because of the increased uncertainty and variability of net load generation caused by high penetrations of renewable energy. In this paper, connected multiple microgrids reconfiguration strategy is proposed while considering renewable generation uncertainty. Reconfiguration strategy is proposed to aggregate different load patterns belonging to various microgrids and match them with a certain microgrid. Then, the new aggregation of loads is aimed to improve the load factor of each MG within the network. The contribution of this study is to solve the peak load problem within the power network without affecting the costumers’ life routine by using a mixed integer linear programming model for the dynamic proactive network reconfiguration of connected microgrids. The formulated model aims to maximize the load factor of each microgrid in the network and flatten the peak load curve without disturbance for the households’ consumption patterns. The proposed model determines the near optimal topology for the network, The proposed mixed integer linear programming model is used to solve the reconfiguration problem with respect to distribution losses, microgrids output and households’ consumption. Moreover, a linearization approach of the load factor equation is proposed to reduce the model complexity and computational time. An artificial dataset contains 3 microgrids and 15 households is used to validate the model performance. The obtained results show that the network reconfiguration could affect the average demand of each individual MG by reducing the fluctuations within the curve. Therefore, the load factor for the three microgrids increases with a percentage ranges from 8% to 53% in comparison with the unconnected system.

A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.

Optimal operating scheme of neighborhood energy storage communities to improve power grid performance in smart cities

In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the second NESC (large-scale) that considers 14 consumer communities is analyzed for the most complete case study (Case 2). Within each NESC, consumer communities are differentiated by the household income and the types of SBs (individual and shared) that support each community. The results corroborate the applicability of the MILP model to real case studies on a diverse scale, guaranteeing the efficient use of PG at the same time that each SB seeks the most optimized operation.

Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system

As the traditional electricity grid transitions to the smart grid (SG), some emerging issues such as increased renewable energy penetration in the power system that cause load unbalances require new control methods. Storage of energy seems to be the best option to struggle with such issues. In this manner, energy storage technologies ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. In this study, a grid condition monitoring user-interface and control algorithm is developed for the peak load reduction and supply-demand balancing in a SG system by using an energy storage unit. For this purpose, a battery energy storage system (BESS) is designed, scaled and integrated into the SG didactic test system, designed by the De Lorenzo Company. Online grid condition monitoring and control software is developed for grid connected photovoltaic (PV) system and the BESS in the LabVIEW? program. Moreover, an algorithm is developed that provides the conditions for the integration of the BESS into the system. The proposed algorithm is tested with real daily load data of the Manisa province in Turkey. Also, various case studies are performed to validate the effectiveness of the algorithm. Consequently, the proposed algorithm provides an average load factor improvement of 8.46% and the algorithm-controlled BESS increases the revenue of the system by 3.51% compared to the grid-connected PV system alone.

Optimal load management strategy under off-peak tariff riders in UTeM: a case study

Demand response (DR) program through tariff initiative has been established in Malaysia since 1990. The available time of use (TOU) tariff focuses on providing price signals to consumers, especially from industrial and commercial sectors. In achieving a certain standard for off-peak tariff rider (OPTR) initiative to receive discount rate, consumers must improve load factors compared to the baseline declared. However, not all consumers are able to commit. In Universiti Teknikal Malaysia Melaka (UTeM), the TOU (C1-OPTR) tariff is proposed and applied when the available cost discount of 20% can be enjoyed by sustaining the load factor improvement (LFI). A simulator projected a flexible optimal load profile referred by the energy management team to achieve the university's sustainable energy management goal. Thus, securing the LFI would allow the energy consumption (kWh) and peak demand (kW) to be managed concurrently. As for testing results for two buildings, the load factor improves to 0.40, and the maximum demand reduces by about 35 kW. When getting the 20% discount for the OPTR scheme, the total cost saving is forecasted approximately USD 29,441.40 yearly. The current pilot project presents a positive sign with the peak demand reduction and load factor improvement close to the simulator's optimal profile.

Demand Side Management Using Battery Energy Storage System for a Sample Residential Load of Pakistan

This research is a step towards the sustainable energy development through the mitigation of the electrical energy crisis using the batteries for not only solving the intermittence but also for demand side management., mostly faced when the energy demand reaches the peak value. An algorithm is presented for the optimal usage of energy by demand side management (DSM) through peak shaving using battery energy storage system (BESS) in the residential areas for developing countries like Pakistan. The data set used is the PRECON data set based on the original demand data collected of 42 houses of Lahore, Pakistan for years 2018-2019. The load profiles for the individual and group of houses are used and analyzed. Daily, monthly and annual load factors are calculated for the groups of houses based on the stochastic data based on realistic data. Daily load factors are improved through demand side management technique such as peak shaving at individual and municipal level. Loads are categorized from the given load profiles into base load, peak load and mid-range load. The cost advantage related to the type of the battery is also considered. Research concludes to the advantages of peak shaving, load factor improvement, load profile improvement and possibility of providing optimal and reliable energy with reasonable energy prices.

A Constrained Programming-Based Algorithm for Optimal Scheduling of Aggregated EVs Power Demand in Smart Buildings

In this paper, a novel cooperative charging strategy for electric vehicles tuned by a constraint programming algorithm has been proposed. The implemented model handles heterogeneous and large-scale residential areas by not only reducing the EV peak charging load, but also improving the user satisfaction levels. The evaluation of the capability of the proposed model at both individual and aggregated levels is considered through various scenarios. The simulated results prove the potential of the proposed cooperative EV strategy in outperforming the uncoordinated EV charging model in terms of peak-to-average ratio reduction, user satisfaction level, and load factor improvement in addition to the suppression of the peak load increase. Furthermore, comparative analysis with existing models shows that the proposed algorithm can manage more complex policies and is performed significantly and efficiently.

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

A Dynamic and Proactive Multi-Microgrid Network Reconfiguration Model for Load Factor Improvement

Simulation model for evaluation of ergonomic load in the use of exoskeletons

For the analysis of musculoskeletal workload and associated potential hazardous working conditions as well as musculoskeletal symptoms in worker populations among various occupations different approaches are taken, and extensive data collection studies are being accomplished. Questions are targeted on musculoskeletal workload and associated potentially hazardous working conditions can be categorized into seven indices (force, dynamic and static load, repetitive load, climatic factors, vibration and ergonomic environmental factors). Evaluation focus on standing, sitting, walking and uncomfortable postures, the indices constitute a brief overview of the main findings on musculoskeletal workload and associated potentially hazardous working conditions. The validity of the findings is fair when compared with an index of psychosocial working conditions and discomfort during exposure to physical loads. Worker groups with contrasting musculoskeletal loads can be differentiated on the basis of the indices and other factors. It appears that most indices and factors show significant associations with low back and/or neck-shoulder symptoms. This simulation model NIOSH analysis can be used as a simple and quick approach to identify worker groups in which a more thorough ergonomic analysis indicates possible improvements in load factor using particular exoskeletons at work.

Impact of Load Factor on Distinct Feeders of 132/11 kV Grid Station in Distribution Network

This study involves a discussion of power load factors, which are important terms for power systems. The advanced load factor is suited to the power system from the power consumption site to the power station and has been studied for various feeders as Tube well, New Petaro, LMC, OCF-I, OCF-II, Old Petaro and Allama I.I Qazi of 132/11 kV grid station Jamshoro. What needs to be researched is the relatively low load factor; due to the load factor, Hyderabad Power Supply Company faces many problems such as transformer tripping, conductive heat up equipments, breakdown of insulation & reactive power failure, all of which are bad main cause of distribution system. To analyse the load impact on the distribution losses of the 07 feeders on the 132/11 kV gird station during collection & to investigate the power loss caused by load factor, the calculated peak power & minimum load data loss sources at distinct load times. After analysis, it is proved that reducing peak load to minimize peak demand and minimum load during peak period, increase b/w average load during bed period and stabilize the power distribution system to improve load factor and increase unit load factor savings are done.