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

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2409 ◽  
Author(s):  
Arslan Bashir ◽  
Mahdi Pourakbari Kasmaei ◽  
Amir Safdarian ◽  
Matti Lehtonen
Keyword(s):  
Demand Response ◽  
Programming Model ◽  
Residential Building ◽  
Hot Water ◽  
Mixed Integer ◽  
Efficient Utilization ◽  
Space Cooling ◽  
Pv Generation ◽  
Heating Loads ◽  
And Storage

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.

scholarly journals Impact of demand response on BIPV and district multi-energy systems design in Singapore and Switzerland

2021 ◽  
Vol 2042 (1) ◽  
pp. 012096
Author(s):  
Christoph Waibel ◽  
Shanshan Hsieh ◽  
Arno Schlüter
Keyword(s):  
Demand Response ◽  
Programming Model ◽  
Systems Design ◽  
Energy Systems ◽  
Mixed Integer ◽  
Peak Shaving ◽  
Climate Conditions ◽  
Building Integrated Photovoltaics ◽  
Energy Hub ◽  
The Impact

Abstract This paper demonstrates the impact of demand response (DR) on optimal multi-energy systems (MES) design with building integrated photovoltaics (BIPV) on roofs and façades. Building loads and solar potentials are assessed using bottom-up models; the MES design is determined using a Mixed-Integer Linear Programming model (energy hub). A mixed-use district of 170,000 m2 floor area including office, residential, retail, education, etc. is studied under current and future climate conditions in Switzerland and Singapore. Our findings are consistent with previous studies, which indicate that DR generally leads to smaller system capacities due to peak shaving. We further show that in both the Swiss and Singapore context, cost and emissions of the MES can be reduced significantly with DR. Applying DR, the optimal area for BIPV placement increases only marginally for Singapore (~1%), whereas for Switzerland, the area is even reduced by 2-8%, depending on the carbon target. In conclusion, depending on the context, DR can have a noticeable impact on optimal MES and BIPV capacities and should thus be considered in the design of future, energy efficient districts.

scholarly journals Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2810 ◽  
Author(s):  
Keon Baek ◽  
Woong Ko ◽  
Jinho Kim
Keyword(s):  
Demand Response ◽  
Management System ◽  
Reference Model ◽  
Storage System ◽  
Operating Cost ◽  
Energy Resource ◽  
Residential Building ◽  
Mixed Integer ◽  
Distributed Energy ◽  
Proposed Model

This study proposes optimal day-ahead demand response (DR) participation strategies and distributed energy resource (DER) management in a residential building under an individual DR contract with a grid-system operator. First, this study introduces a DER management system in the residential building for participation to the day-ahead DR market. The distributed photovoltaic generation system (PV) and energy-storage system (ESS) are applied to reduce the electricity demand in the building and sell surplus energy on the grid. Among loads in the building, lighting (LTG) and heating, ventilation, and air conditioning (HVAC) loads are included in the DR program. In addition, it is assumed that a power management system of an electric vehicle (EV) charging station is integrated the DER management system. In order to describe stochastic behavior of EV owners, the uncertainty of EV is formulated based on their arrival and departure scenarios. For measuring the economic efficiency of the proposed model, we compare it with the DER self-consuming operation model without DR participation. The problem is solved using mixed integer linear programming to minimize the operating cost. The results in summer and winter are analyzed to evaluate the proposed algorithm’s validity. From these results, the proposed model can be confirmed as reducing operation cost compared to the reference model through optimal day-ahead DR capacity bidding and implementation.

scholarly journals Performance Modeling Comparison of a Solar Combisystem and Solar Water Heater

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
John L. Sustar ◽  
Jay Burch ◽  
Moncef Krarti
Keyword(s):  
Energy Savings ◽  
Residential Building ◽  
System Size ◽  
Energy Performance ◽  
Hot Water ◽  
Space Heating ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water ◽  
Heating Loads

As homes move toward zero energy performance, some designers are drawn toward the solar combisystem due to its ability to increase the energy savings as compared to solar water heater (SWH) systems. However, it is not trivial as to the extent of incremental savings these systems will yield as compared to SWH systems, since the savings are highly dependent on system size and the domestic hot water (DHW) and space heating loads of the residential building. In this paper, the performance of a small combisystem and SWH, as a function of location, size, and load, is investigated using annual simulations. For benchmark thermal loads, the percent increased savings from a combisystem relative to a SWH can be as high as 8% for a 6 m2 system and 27% for a 9 m2 system in locations with a relatively high solar availability during the heating load season. These incremental savings increase significantly in scenarios with higher space heating loads and low DHW loads.

scholarly journals A set of comprehensive indicators to assess energy flexibility: a case study for residential buildings

2019 ◽  
Vol 111 ◽  
pp. 04044
Author(s):  
Francesco D’Ettorre ◽  
Marcus Brennenstuhl ◽  
Anjukan Kathirgamanathan ◽  
Mattia De Rosa ◽  
Malcolm Yadack ◽  
...  
Keyword(s):  
Demand Response ◽  
Residential Buildings ◽  
Renewable Energy Sources ◽  
Residential Building ◽  
Heating System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Building Model ◽  
Capacity Change ◽  
External Signals

The increasing share of renewable energy sources in the power industry poses challenges for grid management due to the stochastic nature of their production. Besides the traditional supplyside regulation, grid flexibility can also be provided by the demand side. Demand-Response is an attractive approach based on adapting user demand profiles to match grid supply constraints. Nevertheless, defining the flexibility potential related to buildings is not straightforward and continues to pose challenges. Commonly accepted and standardized indicators for quantifying flexibility are still missing. The present paper proposes a new quantification methodology to assess the energy flexibility of a residential building. A set of comprehensive indicators capturing three key elements of building energy flexibility for demand response, notably, capacity, change in power consumption and cost of the demand response action have been identified. The proposed methodology is applied to a residential building, whose heating system is controlled by means of a model predictive control algorithm. The building model is developed on the basis of the experimental data collected in the framework of a European Commission supported H2020 research project Sim4Blocks, which deals with the implementation of demand response in building clusters. The optimal control problem has been investigated by means of mixed-integer linear programming approach. Real time prices are considered as external signals from the grid driving the DR actions. Results show that the proposed indicators, presented in the form of daily performance maps, allow to effectively assess the energy flexibility potential through its main dimensions and can be easily used either by an end-user or a grid-operator perspective to identify day by day the best DR action to be implemented.

An optimization model for annual harvest planning

2004 ◽  
Vol 34 (8) ◽  
pp. 1747-1754 ◽  
Author(s):  
Jenny Karlsson ◽  
Mikael Rönnqvist ◽  
Johan Bergström
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Road Maintenance ◽  
Continuous Variables ◽  
Home Base ◽  
The Road ◽  
Wood Processing ◽  
Production Capacities ◽  
And Storage

The problem we consider is annual harvesting planning from the perspective of Swedish forest companies. The main decisions deal with which areas to harvest during an annual period so that the wood-processing facilities receive the required amount of assortments. Each area has a specific size and composition of assortments, and the choice of harvesting areas affects the production level of different assortments. We need to decide which harvest team to use for each area, considering that each team has different skills, home base, and production capacities. Also, the weather and road conditions vary during the year. Some roads cannot be used during certain time periods and others should be avoided. The road maintenance cost varies during the year. Also, some areas cannot be harvested during certain periods. Overall decisions about transportation and storage are also included. In this paper, we develop a mixed integer programming model for the problem. There are binary variables associated with harvesting, allocation of teams, and road-opening decisions. The other decisions are represented by continuous variables. We solve this problem directly with CPLEX 8.1 within a practical solution time limit. Computational results from a major Swedish forest company are presented.

Optimal Electric Power Management in a Residential Building Using Photovoltaic and Storage Battery

2012 ◽  
Author(s):  
Tsukasa Demizu ◽  
Shunji Umetani ◽  
Hiroshi Morita
Keyword(s):  
Electric Power ◽  
Power Management ◽  
Energy Cost ◽  
Residential Building ◽  
Mixed Integer ◽  
Storage Battery ◽  
Integer Programming Problem ◽  
Power Management System ◽  
Mixed Integer Programming Problem ◽  
And Storage

We consider the electric power management system facilitated the photovoltaic and storage battery. The total energy cost is derived by solving 0-1 mixed integer programming problem for several scenarios.

scholarly journals Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 936
Author(s):  
Jingjing Zhai ◽  
Xiaobei Wu ◽  
Zihao Li ◽  
Shaojie Zhu ◽  
Bo Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Programming Model ◽  
Energy System ◽  
Linearization Method ◽  
Mixed Integer ◽  
Operation Model ◽  
Piecewise Linearization ◽  
Constraints Network ◽  
Operation Scheduling

An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

scholarly journals An enhanced operation model for energy storage system of a typical combined cool, heat and power based on demand response program: The application of mixed integer linear programming

2018 ◽  
Vol 40 (1) ◽  
pp. 47-74 ◽  
Author(s):  
Amirhossein Eshraghi ◽  
Gholamreza Salehi ◽  
Seyedmohammadreza Heibati ◽  
Kamran Lari
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Residential Building ◽  
Mixed Integer ◽  
Demand Response Program ◽  
Micro Grid ◽  
The Cost

A model for operating an energy hub-based multiple energy generation micro-grid is optimized using the demand response program. The optimized objective model is validated against energy demand of a residential building in Tehran, Iran. The mathematical model and optimal analysis of the proposed tri-generation micro-grid are implemented by using a real-world modelling and considering the constraints of the storage system, demand response program and the performance of the devices and the power and gas grids. The dynamic optimal operation model is prepared on the basis of the mixed integer linear programming on the subsequent day and is solved to minimize the costs of energy supply. To demonstrate the improvements, different scenarios are developed so that the renewable energy resources and storages are fed into the combined cool, heat and power system gradually. The results reveal that the inclusion of each element results in a significant improvement in the operational parameters of the micro energy grid. Scenario 1 includes a combined cool, heat and power system alone, Scenario 2 is supplemented with renewable wind and solar energy resources in addition to combined cool, heat and power system and Scenario 3 includes electrical, heat and cold storages in addition to combined cool, heat and power system and renewable energy sources. Scenario 4 is similar to Scenario 3 in terms of equipment, but the only difference lies in the use of the demand response program in the former. Total operational cost is 12.7% lower in Scenario 2 than in Scenario 1, 9.2% lower in Scenario 3 than in Scenario 2 and 8.6% lower in Scenario 4 than in Scenario 3. Practical application: An optimized operation method is prepared for combined cool, heat and power systems running in different operation modes in which renewable energy sources and storages are added to the combined cool, heat and power and the demand response program is applied. The results reveal that the cost of energy supply, including the cost of electricity, gas and pollutant emissions, is reduced and the qualitative parameters of the operation, including efficiency and reliability of building micro-grid, are increased. The proposed algorithm and the evaluation method will enable building operators to plan demand response activity on the residential building in Tehran, while this can be extended to other buildings too.

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