scholarly journals An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 237
Author(s):  
Seyyed Danial Nazemi ◽  
Mohsen A. Jafari ◽  
Esmat Zaidan
Keyword(s):  
Nonlinear Optimization ◽  
Demand Response ◽  
Commercial Buildings ◽  
Mixed Integer ◽  
Programming Approach ◽  
Optimization Approach ◽  
Peak Demand ◽  
Load Scheduling ◽  
Smart Building ◽  
Demand Response Programs

The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.

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.

scholarly journals Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System

2020 ◽  
Vol 10 (21) ◽  
pp. 7406
Author(s):  
Sobhan Dorahaki ◽  
Rahman Dashti ◽  
Hamid Reza Shaker
Keyword(s):  
Demand Response ◽  
Power Distribution ◽  
Distribution System ◽  
Test System ◽  
Mixed Integer ◽  
Distributed Generations ◽  
Grid Topology ◽  
The Cost ◽  
Demand Response Programs ◽  
Outage Management

In this paper, a novel smart outage management system considering Emergency Demand Response Programs (EDRPs) and Distributed Generations (DGs) denoted as SOMSDGsEDRPs is proposed. The EDRPs are provided to decrease the cost of load shading in a time of emergency. The objective function of the problem is proposed to minimize the load shading cost, the DG dispatch cost, the demand response cost and the repair dispatch time for crews. The SOMSDGsDERPs solves an optimization problem that is formulated as Mixed Integer Linear Programming (MILP) taking into account the grid topology constraints, EDRP constraints, DG constraints and crew constraints. The MILP formulation was demonstrated in the GAMS software and solved with the CPLEX solver. The proposed method was tested on the IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder. The results show that the EDRPs and DGs can be effective in decreasing the outage cost and increasing the served load of the distribution power system in a crisis time.

scholarly journals Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1261
Author(s):  
Neda Hajibandeh ◽  
Miadreza Shafie-khah ◽  
Sobhan Badakhshan ◽  
Jamshid Aghaei ◽  
Sílvio Mariano ◽  
...  
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Demand Response ◽  
Electricity Markets ◽  
Demand Curve ◽  
Mixed Integer ◽  
Programming Approach ◽  
Elastic Demand ◽  
Multi Objective ◽  
System Operator

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1147-1173 ◽  
Author(s):  
Ehsan Dehghani ◽  
Mir Saman Pishvaee ◽  
Mohammad Saeed Jabalameli
Keyword(s):  
Supply Chain ◽  
Markov Process ◽  
Mathematical Programming ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Programming Approach ◽  
Second Phase ◽  
Optimization Approach ◽  
Inventory Problem ◽  
Joint Location

This paper introduces a joint location-inventory problem, in which facilities become temporarily unavailable. A hybrid approach based on the Markov process and mathematical programming techniques is presented to design the distribution network of a supply chain in an integrated manner. In the first phase, the Markov process derives some performance features of inventory policy. In the second phase, using outputs of the Markov process, the location-inventory problem is formulated as a mixed-integer nonlinear programming model. Moreover, a robust possibilistic programming approach is utilized, which is able to provide a more stable supply chain structure under almost all possible values of imprecise parameters. Since the proposed problem is complicated to solve by means of exact methods, we develop a simulated annealing algorithm in order to find near-optimal solutions in reasonable computational times. The obtained computational results reveal the efficiency and effectiveness of the proposed solution approach. Finally, some insights are provided and the performance of the proposed robust optimization approach is compared to traditional possibilistic chance constrained method.

scholarly journals Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3666 ◽  
Author(s):  
Mahmoud M. Gamil ◽  
Makoto Sugimura ◽  
Akito Nakadomari ◽  
Tomonobu Senjyu ◽  
Harun Or Rashid Howlader ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Sea Water ◽  
Peak Load ◽  
Mixed Integer ◽  
Japanese Island ◽  
Total System ◽  
System Cost ◽  
Optimal Sizing ◽  
Demand Response Programs

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

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