Optimal day-ahead scheduling of distributed generations and controllable appliances in microgrid

Firoozi, Hooman; Khajeh, Hosna

doi:10.1109/sgc.2016.7883453

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…This kind of DR program has been implemented in small commercial customers or residential customers [15]. In this regard, the operator can switch the appliances on or off in the limited periods of each year or season.…”

Section: Direct Load Controlmentioning

confidence: 99%

Role of Smart Homes and Smart Communities in Flexibility Provision

Khajeh¹,

Zheng²,

Firoozi³

et al. 2021

Flexibility in Electric Power Distribution Networks

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The impacts of decentralization as well as the high penetration of renewable energy resources into power systems have created a necessity to change the operation and management of the power systems in recent years. Although the effects of renewables at all power system levels can be seen, it is more likely to become an issue, especially in distribution networks due to the increased integration of small-scale renewable generation units (e.g. domestic PV panels). However, the output of renewable energy sources (RES) is naturally variable and intermittent in very short-term periods due to their dependency on environmental weather related factors. Therefore, there might exist a constant imbalance between generation and demand which results in the instability of the power system. In order to prevent these instabilities, the electrical systems must become more flexible, meaning that the system must respond to the probable fluctuations rapidly, consistently, and adequately. To do so, all the potential flexibilities in the electrical systems should be deployed. Thus, the flexible capacities have to be effectively managed and controlled in a smart way by the system operators including distribution system operators (DSO) and transmission system operators (TSO) [1].At the distribution system level, the flexible energy resources are mostly located in the demand side where the majority of consumers are generally residential households/areas. These flexible resources (which can include battery energy storage systems, electric vehicles, flexible loads, demand response, etc.) are able to contribute to enhancing the grid's flexibility by means of different operation and management methods. It is noticeable that in distribution networks, the complexity of the network and also the high amount of end-users and flexible resources worsen the process of dealing with all the end-users individually. Therefore, in order to unlock a considerable amount of flexibility in distribution networks, the system operators need to manage these flexibilities in an aggregated manner in order to be able to utilize them during critical situations. The aggregated management is possible with advanced ICT technologies such as smart management systems in homes or in energy communities with group of homes [2]. This chapter is focused on the role of smart homes and communities in providing demand-side flexibility to the grid. Several energy management methods and approaches for smart homes using home energy management systems (HEMS) for flexibility provision has been previously proposed [3]. These approaches are mostly based on demand-side management by providing dynamic prices to the rational end-users equipped with HEMS and/or taking advantage of demand response programs (DRPs). DRPs are mostly deployed to incentivize customers to react to external signals. The chapter will present the popular DRPs that can reshape the consumption pattern of smart homes and communities. In addition, the flexible resources of residential households and communities are discus...

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Section: Direct Load Controlmentioning

confidence: 99%

Role of Smart Homes and Smart Communities in Flexibility Provision

Khajeh¹,

Zheng²,

Firoozi³

et al. 2021

Flexibility in Electric Power Distribution Networks

Self Cite

View full text Add to dashboard Cite

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“…Day-ahead scheduling comprises switching controllable appliances and determining the optimal input and output of the microgrid. The main purpose of scheduling can vary from maximizing the social welfare of the microgrid [20] to minimizing the total operation costs [21]. A case study done by [22] has shown that employing a combination of the mentioned methods is capable of reducing emissions and voltage and current variations significantly, while showing that the electrical grid constraints have a powerful impact on DERs.…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

Nazemi

Mahani

Ghofrani

et al. 2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.

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