Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

Abstract

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

“…Improving the reliability and accuracy of forecasts can increase the competitiveness of solar electricity in power markets. Several methods have been proposed in recent years aiming to improve the accuracy of forecasts [1] and the solar photovoltaic grid efficiency [2][3][4][5].…”

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

“…In this study, the two key reasons for choosing the HVAC system for contributing to the DR program are: 1) in countries such as Finland, the HVAC systems make up a significant portion of electricity power demand and so considerably affect the daily load demand, and 2) the customers' comfort level is maintained by defining a temperature bound for them while for the other appliances, it is hard to find a way for specifying their comfort level in a satisfactory bound. In order to consider the dynamics of the environmental temperature, this paper utilizes a two-capacity model for the house system [23], [24], see Fig. 1.…”

Costs of Demand Response from Residential Customers’ Perspective

“…Moreover, adding new conventional power plants is economically not preferred because they will match the demand during peak hours but may stay idle during off-peak hours and hence results in wasted capacity. Adding more renewable energy sources may not solve the problem due to their intermittent nature and low reliability [6,7]. Energy storage is considered to be one of the key components to match demand and supply in the future smart grid.…”