An overview of load management techniques in smart grid

Mahmood, Anzar; Javaid, Nadeem; Khan, Muhammad Asghar; Razzaq, Sohail

doi:10.1002/er.3350

Cited by 47 publications

(36 citation statements)

References 54 publications

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“…Further reading for recent trends in DSM , DR programs and HEMS are suggested as [5][6][7]. In [5], the authors provided a compact survey in terms of HEMS.…”

Section: Related Workmentioning

confidence: 99%

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Realistic Scheduling Mechanism for Smart Homes

et al. 2016

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Abstract:In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.

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“…Further reading for recent trends in DSM , DR programs and HEMS are suggested as [5][6][7]. In [5], the authors provided a compact survey in terms of HEMS.…”

Section: Related Workmentioning

confidence: 99%

“…Finally they discussed existing research work conducted in response to scheduling and computational complexity. The authors of [6] gave an extensive survey with respect to load management strategies developed in recent years. Authors discussed strategies to meet different objectives relating to the concept of SG.…”

Section: Related Workmentioning

confidence: 99%

Realistic Scheduling Mechanism for Smart Homes

et al. 2016

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“…Methods in this category are commonly known as Demand Response (DR) [14][15][16][17] and used to avoid peaks of high demand (to reduce cost) and blackouts (due to consumption larger than available generation). To avoid confusion, we use the term "Demand Response" only to refer to methods that implement a demand management from the supply-side.…”

Section: Demand Management From the Supply-sidementioning

confidence: 99%

Energy Management in Prosumer Communities: A Coordinated Approach

2016

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Abstract:The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer's ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer's quality of life.

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“…DSM programs will only be effective if power consumers take an active part. Hence, different pricing mechanisms are also designed [4][5][6][7] to motivate power consumers. Advertising "day ahead per hour price" of electricity is a major pricing mechanism, and shifting electric load to low-priced hours is a promising solution to lower electricity bills and improve the Peak to Average Ratio (PAR) regarding power usage.…”

Section: Introductionmentioning

confidence: 99%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

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Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at "low-priced" hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,"User Comfort Level (UCL)" is formulated on the basis of user preferences on cost saving, tolerance in delay regarding use of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

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An overview of load management techniques in smart grid

Cited by 47 publications

References 54 publications

Realistic Scheduling Mechanism for Smart Homes

Realistic Scheduling Mechanism for Smart Homes

Energy Management in Prosumer Communities: A Coordinated Approach

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

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