Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve ... more 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.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve ... more 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.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and en... more 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.
Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve ... more 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.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve ... more 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.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and en... more 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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