Loading...
8 results
Search Results
Now showing 1 - 8 of 8
- Goal Programming Approach for Energy Management of Smart BuildingPublication . Foroozandeh, Zahra; Ramos, Sérgio; Soares, João; Vale, ZitaIn this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer; (ii) it is assumed a single CP for the entire building; (iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a Multi-Objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-Objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.
- Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming modelPublication . Foroozandeh, Zahra; Ramos, Sérgio Filipe Carvalho; Soares, João; Vale, Zita; Gomes, AntonioNowadays, the buildings have an important role in the high demand for electrical energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage device in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over a long time period to minimize the electricity cost of the building. The proposed model is designed by a Mixed Binary Linear Programming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported.
- Robust Energy Scheduling for Smart Buildings Considering Uncertainty in PV GenerationPublication . Foroozandeh, Zahra; Tavares, Ines; Soares, João; Ramos, Sérgio; Vale, ZitaThe fast growth of renewable energy sources in the residential building led to a complex problem related to the energy management system: the uncertainty associated with the forecast of photovoltaic power generation. To solve this challenge, this paper proposes a robust optimization model to obtain the optimal solution for the worst-case scenario of photovoltaic generation. A Mixed Binary Linear Programming problem is transformed into a trackable robust counterpart to provide immunity against the worst-case realization. Through the budget of uncertainty, the risk of the solution can be adjusted. The results demonstrate that the influence of Battery Energy Storage System and Electric Vehicles against uncertainties leads to higher economic gains up to 6% reduction.
- Shared PV Production in Energy Communities and Buildings ContextPublication . Ramos, Sérgio; Foroozandeh, Zahra; Soares, João; Tavares, Inés; Faria, Pedro; Vale, ZitaAcross Europe, householders have taken the opportunity to produce their electricity, helping them to reduce their electricity bill as well as reducing carbon emissions, by installing rooftop Photovoltaic Panels (PV) on their buildings. New adequate business models are needed for improving the sharing of PV between consumers in a community. Both technical and economic aspects should be considered in a clear way for consumers to understand and benefit from the community. In this paper, an overview of energy communities is provided to support the innovative PV sharing models for buildings, which are proposed in a way to be clear for community members. The developed concepts are supported by the formulation of the optimization problem to be solved by the community manager.
- Optimal Contract Power and Battery Energy Storage System Capacity for Smart BuildingsPublication . Foroozandeh, Zahra; Ramos, Sérgio; Soares, João; Vale, ZitaThis paper proposed a Mixed Binary Linear Programming (MBLP) approach to find the optimal size of some components of a Smart Building (SB) attempting to reduce the overall cost. The considered SB is equipped with local resources such as Photovoltaic (PV) panels, Electrical Vehicles (EVs), and the Battery Energy Storage System (BESS). Moreover, the SB is only connected with the grid by an Energy Management System (EMS) in which the whole SB has a single Contract Power (CP) such that EMS manages the power flow among external grid, local resources, apartments, and common services, for the goal of reducing the electricity bill. Hence, the wrong choice of CP and BESS capacity will impose unnecessary charges on the electricity bill. As a results, EMS has played a crucial role in SB in determining the best CP and BESS values. The obtained results of this work show the efficiency of the model in which by finding the optimal capacity of CP and BESS, the electricity bill improves by a 34% reduction
- Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization ApproachPublication . Foroozandeh, Zahra; Ramos, Sérgio Filipe Carvalho; Soares, João; Vale, ZitaGenerally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction
- Optimizing Energy Consumption of Household Appliances Using PSO and GWOPublication . Tavares, Inês; Almeida, José; Soares, João; Ramos, Sérgio; Vale, Zita; Foroozandeh, ZahraDue to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.
- Single contract power optimization: A novel business model for smart buildings using intelligent energy managementPublication . Foroozandeh, Zahra; Ramos, Sérgio; Soares, João; Vale, Zita; Dias, MaurícioTypically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably.