A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

The Benefits of Enterprise Data Warehouse Implementation in Cloud vs. On-premises

When we think about running enterprise applications on-premises, enterprises do two things for their servers, databases, and storage. Enterprises provision for peaks and put a lot of infrastructures to handle peak demand, although a lot of this capacity is not used at normal times. The other thing is a few instances that each application needs to have, typically between five and six. Multiplying this number by many times due to various applications causes a lot of costs and creates capacity that is not used. For such reasons, the enterprise applications in the cloud seem reasonable. In the cloud, two things are possible again. Instead of overprovisioning for peaks, enterprises can scale the capacity on on-demand and spin up instances on demand. This means a certain amount of cost-saving by running at a normal level instead of overprovisioning. In this paper, various factors will be considered, and the benefits for enterprise data warehouse implementation in the cloud vs. on-premises will be stated. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC’s yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use.

Tripartite Dynamic Game among Government, Bike-Sharing Enterprises, and Consumers under the Influence of Seasons and Quota

After the cast ban on bike-sharing was lifted, bike-sharing entered the quota period. This notion means that the management of bike-sharing began to change from the unified to the diversified government governance, including all sectors of society. This work creates a dynamic game model based on the tripartite interest relationship among the government, bike-sharing enterprises, and consumers, and introduces the government quota policy and seasonal characteristics of bike-sharing into the game model. This model explores the multi-stage dynamic game process among the government, bike-sharing enterprises, and consumers. We draw the following conclusions. The government’s quota policy was effective during peak demand for bike-sharing, but not before the off-peak season. Through the case studies, we verify the feasibility of the government to relax the regulation appropriately in the peak season. We also changed the punishment and reward intensity of bike-sharing enterprises to consumers in the case studies and analyzed the influence of regulation intensity of bike-sharing enterprises on consumer behaviors. The final suggestion is that the government should appropriately relax regulation during peak demand season to reduce costs and strengthen regulation before the off-season of bike-sharing demand. Bike-sharing enterprises should maintain a high level of regulation on consumers, and a low level of regulation has no constraint on consumer behaviors.

Scenarios Simulation and Analysis on Electric Power Planning Based on Multi-scale Forecast: A Case Study of Taoussa, Mali from 2020 to 2035

A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.