Peak Load Profile Research Articles

Estimating synthetic load profile based on student behavior using fuzzyinference system for demand side management application

This paper proposes a novel approach of estimating synthetic load profiles based on the electrical usage behavior using the fuzzy inference system (FIS) for demand side management (DSM). In practice, DSM is utilized to change the pattern of electrical energy consumed by end-users to modify the load profile by manipulating the price of the electricity. This study focuses on the energy consumption consumed by students who are paying electricity bills indirectly. Therefore, the effectiveness of conventional DSM methods on this user requires further investigation. In this study, the FIS estimates the synthetic load profile based on the student?s behavior profile. Then, three DSM techniques: load clipping, load shifting, and load conservation, are applied to the electrical usage behavior model. The FIS estimates the synthetic load profile based on the modified electrical usage behavior model with these DSM techniques. From this estimation, the synthetic load profiles are analyzed and compared to evaluate the effectiveness of the DSM methods on the students. The result shows that the FIS estimates the synthetic load profile satisfactorily. Also, load conservation is the most effective technique in reducing the peak load profile and power consumption for this type of user. Conclusively, the result implies that the proposed methodology can be used to evaluate the effectiveness of the DSM method to reshape the load profile.

Limited-Stop High-Frequency Service Design: Reducing In-Vehicle Congestion

Limited-Stop (LS) bus services have recently proved to be essential for improving user welfare and reducing operators’ costs in many cities. The design of LS services has been mainly focused on increasing fleet efficiency and reducing the passengers’ travel time. In this work, we change the focus of LS service design towards the user’s comfort. Given a fixed-size fleet (fixed costs) and a fixed demand on a very high-frequency bus corridor, we propose an algorithm to minimize the peak load profile, combining the usual All-Stop (AS) and one additional LS service, finding the set of stops for the LS service and the fleet split. The strategy is proved in a set of statistically generated corridors, showing average capacity reductions > 20% at a cost of a marginal travel time increase. Analyzing the peak value in the load profile of all simulated corridors, the number of cases where the majority of users would find a seat on the bus increases from 15% to 53%, making the services much more attractive without increasing the costs.

Battery Life Enhancement in a Hybrid Electrical Energy Storage System Using a Multi-Source Inverter

This paper introduces a new topology using a multi-source inverter with the intention of reducing the battery current and weight, while enhancing the battery life and increasing the driving range for plug-in electric vehicles, with the combination of a battery and an ultracapacitor (UC) as storage devices. The proposed topology interconnects the UC and battery directly to the three-phase load with a single-stage conversion using an inverter. The battery life is considerably reduced due to excess (peak) current drawn by the load, and these peak load current requirements are met by connecting the ultracapacitor to the battery, controlled through an inverter. Here, the battery is used to cater to the needs of constant profile energy demands, and the UC is used to meet the dynamic peak load profile. This system is highly efficient and cost-effective when compared to a contemporary system with a single power source. Through a comparative analysis, the cost-effectiveness of the proposed energy management system (EMS) is explained in this paper. Energy and power exchange are implemented with an open-loop control strategy using the PSIM simulation environment, and the system is developed with a hardware prototype using different modes of inverter control, which reduces the average battery current to 27% compared to the conventional case. The driving range of electric vehicles is extended using active power exchange between load and the sources. The dynamics of the ultracapacitor gives a quick response, with battery current shared by the ultracapacitor. As a result, the battery current is reduced, thereby enhancing the driving cycle. With the prototype, the results of the proposed topology are validated.

A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

The smart grid is seen as a power system with real-time communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper, a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service Ethernet network with appropriate quality-of-service profiles. The developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida, and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.

Peak Load Curtailment in a Smart Grid Via Fuzzy System Approach

Among many significant smart grid initiatives and challenges considered by many utilities and within the research community, are those associated with the energy management and conservation, in particular the management of energy demand during peak load periods. In this paper, a novel method for peak load curtailment by using a fuzzy system approach is presented. The proposed method is based on the application of fuzzy logic principles for peak load curtailment in a smart grid environment. The inputs to the system are the utility peak load data consisting of many energy demand scenarios, and the outputs are the necessary demand response power reductions required for the load curtailment during the peak load periods. The proposed method considers different peak load profiles and power consumption sources for multiple city regions. Furthermore, it is adaptable for use in many scenarios, such as those encompassing many input sources of power consumption with diverse input parameters of control (i.e., temperature offsets, duty cycle control, etc.) within numerous city regions. Thus, it can be applied to multiple output variables of control.

A New Approach for Corrective and Preventive Control to Unsolvable Case in Power Networks having DERs

Recently, Korean system operating conditions have gradually approached an upper limit. When a contingency occurs, the power system may have no solutions. Different from the cases of bad initial guesses or the solutions are too close to the solvability boundary in which numerical methods can be applied, for unsolvable cases, the only way to restore solvability would be structure modifications. In this paper, a new approach for corrective and preventive control to such cases is proposed in two steps: (i) finding any solution regardless its feasibility; (ii) for the infeasible solution, make it feasible with additional modifications at load buses having Distributed Energy Resources. The test case built based on the peak load profile of 2008 by KEPCO including 1336 buses is analyzed. Since reactive power compensation is optimized to restore solvability, all demands are met, therefore no blackouts happen. The proposed method was integrated in the LP program designed by power21 Corporation.

Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems

Recent development in small renewable/clean generation technologies such as wind turbines, photovoltaic, fuel cells, microturbines and so on has drawn distribution utilities' attention to possible changes in the distribution system infrastructure and policy by deploying distributed generation (DG) in distribution systems. In this study, a methodology has been proposed for optimally allocating wind-based DG units in the distribution system so as to minimise annual energy loss. The methodology is based on generating a probabilistic generation–load model that combines all possible operating conditions of the wind-based DG units and load levels with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer non-linear programming (MINLP), with an objective function for the system's annual energy losses minimise. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, discrete size of the DG units, maximum investment on each bus, and maximum penetration limit of DG units. This proposed technique is applied to a typical rural distribution system and compared to the traditional planning technique (constant output power of DG units and constant peak load profile).

A comparison of fuzzy neural networks for the generation of daily average and peak load profiles

Two fuzzy neural network models, based on the multilayer perceptron and capable of fuzzy classification of patterns, are presented in this paper. The first type of fuzzy neural network uses the membership values of the linguistic properties of the past load and weather parameters, and the output of the network is defined as fuzzy class membership values of the forecasted load. The backpropagation algorithm is used to train the network. The second type of fuzzy neural network is developed based on the fact that any fuzzy expert system can be represented in the form of a feedforward neural network. This kind of fuzzy neural network is trained to develop fuzzy logic rules and to find optimal input/ output membership values. A hybrid learning algorithm, consisting of unsupervised and supervised learning phases, is used to train this network. Extensive tests have been performed on a two-year utility data for generation of peak and average load profiles in a 24-hours-ahead time frame, and results for two typica...

An assessment of solar hot water heating in the Washington, D.C. area: Implications for local utilities

A survey of residential solar hot water heating in the Washington, D.C. area is presented with estimates of the total solar energy contribution per year. These estimates are examined in relation to a local utility's peak-load curves to determine the impact of a substantial increase in solar domestic hot water use over the next 20 yr in the area of utility management. The results indicate that a 10% market penetration of solar water heaters would have no detrimental effect on the utility's peak-load profile and could save several million dollars in new plant construction costs.