Standard Load Profiles Research Articles

LOAD FORECASTING FOR MONTHS OF THE LUNAR NEW YEAR HOLIDAY USING STANDARDIZED LOAD PROFILE AND SUPPORT REGRESSION VECTOR: CASE STUDY HO CHI MINH CITY

Load forecasting plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks. For Asian countries (such as Vietnam) that use lunar calendar, one of the most difficult and unpredictable issues is the Lunar New Year (usually in late January or early February). There is a deviation between the solar calendar and the lunar calendar (the load models are not identical). Therefore, it often leads the forecast results of algorithm for this period with large errors. The paper proposes a method of short-term load forecasting by constructing a Standardized Load Profile (SLP) based on the past electrical load data, combining machine learning algorithms Support Regression Vector (SVR) to improve the accuracy of load forecasting algorithms.

LOAD FORECASTING FOR MONTHS OF THE LUNAR NEW YEAR HOLIDAY USING STANDARDIZED LOAD PROFILE AND SUPPORT REGRESSION VECTOR: CASE STUDY HO CHI MINH CITY

Load forecasting plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks. For Asian countries (such as Vietnam) that use lunar calendar, one of the most difficult and unpredictable issues is the Lunar New Year (usually in late January or early February). There is a deviation between the solar calendar and the lunar calendar (the load models are not identical). Therefore, it often leads the forecast results of algorithm for this period with large errors. The paper proposes a method of short-term load forecasting by constructing a Standardized Load Profile (SLP) based on the past electrical load data, combining machine learning algorithms Support Regression Vector (SVR) to improve the accuracy of load forecasting algorithms.

Time series of heat demand and heat pump efficiency for energy system modeling

With electric heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the heat demand and the coefficient of performance (COP) of heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform.

Comparing smart metered, residential power demand with standard load profiles

Abstract Despite their age, residential standard load profiles (SLPs) are a widely used tool to represent the electric power demand of a household. However, the prospect of a future power system relying mainly on renewable and often intermittent, distributed energy resources (DER) is pushing SLPs to their limit. Determining self consumption rates and the potential of demand response through smart home applications or simulating distribution grids, especially the low voltage (LV) level—all require a realistic replication of residential load patterns. However, data of high temporal resolution for individual households is rare. Therefore, several smart meters were installed at participants’ homes within a research project and the measurements are analyzed. For the first time, complete load profiles for a whole year in a temporal resolution of 15 s are used to derive key characteristics. For example, the level of fluctuation is described by different quantiles of the instantaneous power. It is found that the 99% quantile exceeds mean power values by 5 – 10 times, but pooling of households reduces this factor strongly. Other findings, for example that one third to half of the energy is consumed at low power levels (

Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

Improve the accuracy of short-term forecasting algorithms by Standardized Load Profile and Support Regression Vector: Case study Vietnam

Improve the accuracy of short-term forecasting algorithms by Standardized Load Profile and Support Regression Vector: Case study Vietnam

Impact of electromobility on the future standard load profile

Impact of electromobility on the future standard load profile

Small-scale 10 kW wind turbine on-grid connected for power supply of two different consumers

In this paper a power supply system based on a 10-kW wind turbine on-grid connected used for two different consumers is analysed. The proposed wind turbine site is located at coordinates 46°57’41”N 25°56’36” E, in the Ceahlau National Park, on the plateau of Ceahlau Mountain. Nearby there are located the two consumers: the Ceahlau Monastery and the Dochia Chalet. The site wind resource is obtained mainly from NASA Surface meteorology and Solar Energy database. The load demand curves, the annual energy consumption and the peak load are obtained considering the standard load profile for these two particular types of consumers. The on-grid power supply system is analysed using HOMER Energy microgrid modelling software for different tourist’s occupancy. Two different wind turbines (XZ and XL) with rated power of 10 kW are comparatively analysed. Selection of the most suitable wind turbine is realized using the Weighted Product Method (WPM), for which nine attributes are used: annual energy production, capacity factor, renewable fraction, grid purchase, grid sales, initial investment, net present cost, levelized cost of energy, and simple payback. For a given criteria weights, the XL model is found to be the most suitable wind turbine.

Effects of charging battery electric vehicles on local grid regarding standardized load profile in administration sector

Integration of battery electric vehicles (BEV) as load could have an impact on the stability of local grids. With the experiment conducted in a research project, real-time metering data of the charging points was collected, with which the charging process, user behavior and furthermore modeling of default pattern on a working-place charging station park are presented and further discussed in this paper. The simultaneity of load peak from uncontrolled charging processes and from the local load profile could cause local grid bottleneck. A BEV pool model to generate random charging sessions on a workday, which conforms the real behavior in this research case, is proposed. Charging strategies were developed and considered as centralized or decentralized. The simulation shows the influence of delay charging start time and demonstrate the potential of peak load reduction from charging of several BEV and furthermore of a large-scale construction of charging infrastructure.

Residential photovoltaic self-consumption: Identifying representative household groups based on a cluster analysis of hourly smart-meter data

The on-site generation and direct consumption of electricity, so-called self-consumption, with a combined photovoltaic (PV) and battery storage system is becoming increasingly profitable for private households. The profitability of PV self-consumption system largely depends on the match of PV output and the household’s electricity consumption. In energy system modelling, the household’s consumption behaviour is represented by means of a standard load profile. However, the household sector’s heterogeneity is not reflected in one single profile, and the use of only one load profile results in a misjudgement of the profitability of self-consumption. In this study, we present a set of representative household groups that better represent the heterogeneous residential consumption behaviour. The household groups were compiled through the cluster analysis of smart-meter data based on hourly electricity consumption, using household characteristics as explanatory variables. Between the average load profiles of the groups, significant differences were found. Subsequently to the clustering, self-consumption based on a combined PV and battery system was simulated for each household. We found that the achievable level of self-consumption also differs between the groups, which in turn affect the profitability of the PV and battery systems. A statistical analysis revealed that employment and the presence of children are distinguishing factors for the different types of self-consumers. These results suggest that (i) the residential sector is not well represented by a single standard load profile, particularly so in the context of self-consumption modelling. (ii) Different self-consumer types can be identified through socio-demographic characteristics: We found that unemployed households achieve the highest self-sufficiency rates with an average of 40%, the lowest rates with 30% on average occur within households of educated families. (iii) Although the discrepancies are significant, the effect of these differences on profitability is still limited under the current market conditions.

Smart Meter Data-Based Load Profiles and Their Effect on Distribution System State Estimation Accuracy

The constantly spreading smart meters open new possibilities for load profiling. With smart meter measurements, standard customer class load profiles can be updated, customers can be clustered into similarly behaving groups, or individual load profiles can be created. This paper studies how these new smart meter data -based load profiling methods affect the distribution system state estimation accuracy. Extensive simulations were made with a real distribution network containing both medium and low voltage networks. Results achieved with different load profiling methods were compared with each other, with publicly available standard customer class load profiles, and with other currently used load modelling methods. In all studied cases, the new smart meter data -based load profiles provided superior state estimation accuracy. The results presented in this paper should motivate distribution network operators to utilize smart meter measurements more efficiently in load profiling and state estimation.

Application of time-resolved input data for smart grid simulation

Grid control algorithms need to be tested in a simulation before adapting to the real network operation. Typical simulation techniques are load flow based. Thus, an appropriate network model and realistic input data are needed. Varying feed-in and load data may lead to considerably differing loading scenarios. The quality and plausibility of data is therefore highly important. Usually distribution system operators do not possess time-resolved data for all grid elements. This makes a realistic simulation of large networks complicated. Standard load profiles and historical data may be used. An alternative possibility for generating time-resolved behaviour of all network participants is an agent-based approach. In the project Agent.GridPlan a multi-agent system for distribution network planning is being developed [The project ‘Agent.GridPlan’ (agent-gridplan.net) is funded by the European fund for regional development.]. The system provides active and reactive power time series based on weather data and individual parameters of loads and generators. Using this simulation technique a forecast-based network reconfiguration algorithm is tested in the scope of a 1-year-simulation.

Improving Operation of a 2.5kW SOFC Power System with Supervisory Control

Running SOFC power systems at the edge of efficiency and maximal longevity is important for their successful market deployment. In this paper, we argue that in order to reach this goal, the conventional control solutions do not suffice. In fact, to properly handle variable load conditions and inevitable degradation processes, on-line optimization is needed to adjust the process variables to run the process at the operational optimum. We propose a two-level control system in which the low-level control is upgraded with a supervisory optimizer. The optimization problem targets maximisation of efficiency and minimization of stack temperature violations. It is solved by using the extremum-seeking approach where optimum is sought directly on the process. The results are adjusted references for the low-level controllers. The proposed supervisory optimizer is assessed on a physical model of a 2.5 kW SOFC power system on a standard daily current load profile of residential houses. Simulation results indicate that thanks to the optimizer, efficiency of the system can be improved by about 8% compared to the conventional control scheme with the low-level controllers. Due to its simplicity, the optimizer appears appropriate for practical applications.

Techno-economic comparison of a schedule-based and a forecast-based control strategy for residential photovoltaic storage systems in Germany

In Germany, the huge integration of small photovoltaic (PV) systems into the distribution grid during the past years leads to power quality issues due to the intermittent generation and reverse power flow in periods of low demand. To decrease this impact, different solutions are being investigated. The scope of this paper is to compare different strategies to control the charge power for residential PV storage systems for different load curves and to decide which might be the economically most profitable strategy. For this purpose, three different PV storage system control strategies were analyzed using MATLAB®to perform 1-year simulations on a minute step base. Measured input data from a PV system in the south of Germany were combined with four measured (extreme) load profiles and a standard load profile to conduct a sensitivity analysis. Performance indicators, such as self-consumption ratio (SCR), self-supply ratio (SSR), and share of losses ratio (SLR), were used to compare the different control strategies. Furthermore, an economic analysis of these results was performed to obtain the profitability of every control strategy and to determine the most profitable strategy, considering the household owner’s benefits.

Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake

Adequately accounting for interactions between Low Carbon Technologies (LCTs) at the building level and the overarching energy system means capturing the granularity associated with decentralised heat and power supply in residential buildings. The approach presented here adds novelty in terms of a realistic socioeconomic differentiation by employing dwelling/household archetypes (DHAs) and neighbourhood clusters at the Output Area (OA) level. These archetypes are combined with a mixed integer linear program (MILP) to generate optimum (minimum cost) technology configurations and operation schedules. Even in the baseline case, without any LCT penetration, a substantial deviation from the standard load profile (SLP) is encountered, suggesting that for some neighbourhoods this profile is not appropriate. With the application of LCTs, including heat pumps, micro-CHP and photovoltaic (PV), this effect is much stronger, including more negative residual load, more variability, and higher ramps with increased LCT penetration, and crucially different between neighbourhood clusters. The main policy implication of the study is the importance of understanding electrical load profiles at the neighbourhood level, because of the consequences they have for investment in the overarching energy system, including transmission and distribution infrastructure, and centralised generation plant. Further work should focus on attaining a superior socioeconomic differentiation between households.

Building power demand forecasting

Abstract Buildings acting as flexible loads have been often proposed to mitigate the volatility of renewable energy sources. Thereby, an accurate short-term demand forecast is indispensable for effective demand side management. At the same time, standardized load profiles, commonly used in the distribution grid, are inadequate for load forecasting within building domain. For this PhD, project a novel short-term forecasting model is proposed for that domain. It considers not only residual load, but also scheduled demand response as well as the PV-generation of the building. Moreover, it is not building specific and is, therefore, suitable for area-wide application within building domain.

Impact of Pseudo-Measurements From New Power Profiles on State Estimation in Low-Voltage Grids

Low-voltage state estimation (SE) in distribution power system, because of lack of real-time measurements, must heavily rely on pseudo-measurements, primarily originating from standard load profiles (SLPs). Actual load profiles have been recently suspected of significantly deviating from the SLPs, and research work is ongoing to quantify and characterize this deviation and to synthesize more realistic profiles. The analysis of the propagation of error and uncertainty of load profiles in SE must be investigated, as distribution system SE in distribution is being experimentally deployed, and the use of any available source of data is under consideration. The findings provide hints on the accuracy to be expected and on corrections to be applied to classical pseudo-measurements.

Modelling and multi-scenario analysis for electric heat tracing system combined with low temperature district heating for domestic hot water supply

Low temperature district heating (LTDH) is a cost-efficient way of supplying space heating and domestic hot water (DHW) for buildings in urban areas. However, there is concern that the potential hygiene problems (Legionella) might occur if LTDH is implemented, especially for large buildings. In this study, electric heat tracing system was investigated as a solution to this dilemma. A model of electric heat tracing system for multi-storey buildings was built. Various pipe materials and insulation thicknesses as the parameters of the model were compared to make a comprehensive study. The performance of the electric heating tracing system with LTDH was simulated by taking the user pattern into account. A smart control method based on thermostatic and real-time control was developed, and compared with normal control method. The impact of user pattern was tested by applying standard, stochastic, and real load profiles to the model. The energy and economy performances of electric heat tracing system in different scenarios were simulated by Matlab. The results were compared to the conventional circulation system with the current generation district heating. The electric heat tracing system with LTDH showed good performance on heat loss saving, and it also gave benefits to district heating network by sharing part of the heating load.

Feedforward-Feedback Control of a SOFC Power System: A Simulation Study

Smooth and timely control actions improve the efficiency and durability of solid oxide fuel cell (SOFC) power systems. In this paper a systematic feedforward-feedback approach to control design for SOFC power systems is proposed. The control aim is to regulate the temperature of and the temperature difference over the stack by manipulating the air flow, air inlet temperature and fuel flow. The feedforward part responds to the demanded electrical current and stoichometry of reforming and electrochemical processes. It gives the fuel flow rate needed to achieve the required stack current with a given fuel utilization. The air flow rate is then calculated according to the oxygen consumption in fuel cells and a given air utilization. Feedforward control is combined with the feedback control to adjust the manipulated variables based on the measured system output. Relative gain array (RGA) analysis (Shinskey, 1988) is performed over the system inputs and outputs (I/O) to determine optimal I/O pairing for control. The RGA is calculated from the static gain matrix of the SOFC stack model (Sorrentino et al., 2008). The strongest interactions are found between the cathode outlet temperature and the cathode inlet temperature, the stack temperature difference and the air flow rate, and the stack voltage and the fuel flow rate. Fuel flow rate has a strong influence also on the stack temperature and can be in some situations used to support temperature control. Based on the RGA analysis, two individual PI control loops are determined. In the first loop, the cathode outlet temperature is controlled by the proportional-integral (PI) controller that uses the cathode inlet temperature as the manipulated variable. In the second loop, the stack temperature difference is controlled by the PI controller with the air flow rate as the manipulated variable (see Figure 1). Altogether the control system consists of two PI control loops, making it feasible for practical implementation. The control target is similar as in (Pohjoranta et al., 2015) but it is obtained here with a significantly simpler approach. Anti-windup protection is included in the PI controllers to avoid long settling times caused by the limits on the manipulated variables (Peng et al, 1999). Parameters of the PI controllers are tuned from the open-loop step response experiments by applying the Magnitude Optimum Multiple Integration (MOMI) method (Vranèiæ et al., 1999). The feedforward-feedback control is evaluated on the SOFC stack model (Sorrentino et al., 2008) over a one-day operation cycle using the standard load profile of residential houses (Knight and Ribbering, 2007). The proposed feedforward-feedback control provides good control of the stack temperatures (see Figure 2). Relatively high stack voltage changes are caused by the significant load changes, however, but these can be reduced by combining a SOFC power system with an energy buffer. References Knight, I., Ribbering, H. (eds.) (2007). European and Canadian non-HYAC Electric and DHW Load Profiles for Use in Simulating the Performance of Residential Cogeneration Systems. A Report of Subtask A of FC+COGEN-SIM: The Simulation of Building-Integrated Fuel Cell and Other Cogeneration Systems. Peng Y., Vrancic D., Hanus R. (1996). Anti-windup, bumpless, and conditioned transfer techniques for PID controllers. Control syst. mag., 16, 48-57. Pohjoranta, A., Halinen, M., Pennanen, J., Kiviaho, J. (2015). Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data. Journal of Power Sources, 277, 239-250. Shinskey, F.G. (1988). Process control systems. 3rdedition. McGraw-Hill, New York. Sorrentino, M., Pianese, C., Guezennec, Y.G. (2008). A hierarchical modeling approach to the simulation and control of planar solid oxide fuel cells. Journal of Power Sources, 180, 380-392. Vrancic, D., Peng, Y., Strmcnik, S. (1999). A new PID controller tuning method based on multiple integrations, Control Engineering Practice, 7(5), 623-633. Figure 1

Feedforward-Feedback Control of a SOFC Power System: A Simulation Study

In this paper a feedforward-feedback control design for solid oxide fuel cell (SOFC) power systems is proposed. The feedforward part of the controller reacts to the electrical current demand as well as the stoichometry of electro-oxidation and reforming processes. Feedback part performs corrections of the controlled system output by additional manipulation of system inputs. Based on relative gain analysis (RGA), it is found that two individual PI control loops turn out suitable enough to control the system. Parameters of the PI controllers are tuned on the basis of open-loop step response experiments by applying the Magnitude Optimum Multiple Integration method (MOMI). The proposed feedforward-feedback control is verified on the SOFC stack model over a one-day operation cycle using the standard load profile of residential houses. The main advantage of the proposed controller is its simple structure and easy tuning, which makes it feasible for practical implementation.