Smart Grid Home Area Network for Dynamic Pricing and Demand Management, by Kulkarni, Ozturk, Toeldte

Project Overview

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source:https://www-sciencedirect-com.ezproxyberklee.flo.org/science/article/pii/S037877881730823X#fig0005

The proposed smart grid system uses data from sensors at the Home Area Network (HAN) level to make optimal dynamic pricing decisions for electricity, as well as modulating the power consumption of smart devices.

These kinds of energy control technologies have become more important as we move towards an increased share of intermittent renewable energies in electricity generation, and increasingly electrify domains such as transportation and home heating. Inefficient supply and demand matching in electricity generation can result in large plant-to-plug energy losses, on top of the unavoidable transmission losses.

The technology uses data at the generation level and consumer level to make predictions about future energy supply and demand. In case of imbalance, the technology provides price signals to Home Area Networks, which automatically make energy consumption decisions based on some parameters that the consumer determines, taking advantage of the price elasticity of demand for electricity to bring the system back into balance. Furthermore, the technology would inform consumers about their energy use patterns, and the share of electricity sourced by renewables. This would both allow firms to make more profit and/or displace consumer demand to times that have a higher availability of renewable energies, reducing the environmental footprint of electricity.

Large scale smart grid technologies could also better schedule the ramp up/down of nonrenewable sources, and take advantage of the key performance indicators (KPIs) of available energy resources to optimally allocate the duty cycle of potential energy sources. This is however outside the scope of our work.

DSM Allocation

This DSM depicts components of the Energy Management System and their interactions.

Object Process Diagram - OPD

This Object Process Diagram depicts how the Energy Management System at the Home Area Level works.

SMART Energy Meters is physical and systemic.
Home Area Network of Energy Manager & Interface and SMART Home is informatical and systemic.
SMART Appliances is physical and systemic.
Siemens is physical and systemic.
Schneider Electric is physical and systemic.
Bosch is physical and systemic.
Eco-bee is physical and systemic.
Figure Of Merit is informatical and systemic.
Human is physical and environmental.
SMART thermostats is physical and systemic.
Price is informatical and systemic.
Utility Service Provider is physical and environmental.
Energy Manager & Interface is informatical and systemic.
Consumption of House Electricity is informatical and systemic.
Information is informatical and systemic.
Peak Load of House Electricity is informatical and systemic.
Time of Peak Load of House Electricity is informatical and systemic.
Control Suggestions is informatical and systemic.
Energy Bill is physical and systemic.
SMART Sockets & Plugs is physical and systemic.
SMART Home is physical and systemic.
EV Charging Point is physical and systemic.
House Electricity is physical and systemic.
Electricity from Substation is physical and environmental.
Energy Share Of Renewables is informatical and systemic.
Latency Between Pricing Decisions & Changes In Consumption is informatical and systemic.
Average Price Of Electricity is informatical and systemic.
Accuracy Of Demand Predictions is informatical and systemic.
Cost Per HAN Installation is informatical and systemic.
Load Reduction During Peak Hours is informatical and systemic.
SMART Home exhibits Home Area Network.
SMART Home consists of EV Charging Point, SMART Appliances, SMART Energy Meters, SMART Sockets & Plugs, and SMART thermostats.
House Electricity exhibits Consumption, Peak Load, and Time of Peak Load.
Bosch, Eco-bee, Schneider Electric, and Siemens are Energy Manager & Interface.
Accuracy Of Demand Predictions, Average Price Of Electricity, Cost Per HAN Installation, Energy Share Of Renewables, Latency Between Pricing Decisions & Changes In Consumption, and Load Reduction During Peak Hours are Figure Of Merit.
Average Price Of Electricity relates to Price.
Latency Between Pricing Decisions & Changes In Consumption relates to Energy Manager & Interface.
Cost Per HAN Installation relates to SMART Home.
Load Reduction During Peak Hours relates to SMART Energy Meters.
Accuracy Of Demand Predictions relates to Energy Manager & Interface.
Energy Share Of Renewables relates to SMART Home.
Energy Manager & Interface exhibits Home Area Network.
Measuring is informatical and systemic.
Measuring requires SMART Energy Meters.
Measuring consumes Consumption of House Electricity, Peak Load of House Electricity, and Time of Peak Load of House Electricity.
Measuring yields Information.
Analyzing is informatical and systemic.
Energy Manager & Interface handles Analyzing.
Analyzing consumes Information.
Switching is physical and systemic.
Energy Manager & Interface and Human handle Switching.
Switching affects EV Charging Point, SMART Appliances, and SMART Sockets & Plugs.
Controlling is informatical and systemic.
Energy Manager & Interface and Human handle Controlling.
Controlling affects SMART thermostats.
Computing is informatical and systemic.
Computing requires Utility Service Provider.
Computing yields Price.
Transferring is physical and systemic.
Transferring requires Home Area Network of Energy Manager & Interface and SMART Home.
Transferring consumes Information.
Transferring yields Utility Service Provider.
Recording is physical and systemic.
Recording affects Energy Manager & Interface.
Recording consumes Information.
Implementing is physical and systemic.
Human handles Implementing.
Implementing requires Control Suggestions.
Implementing affects Price.
Integrating is informatical and systemic.
Integrating requires Price.
Integrating yields Energy Bill.
Ditributing is physical and environmental.
Ditributing requires Electricity from Substation.
Ditributing yields House Electricity.
Powering is physical and systemic.
Powering affects EV Charging Point, SMART Appliances, SMART Energy Meters, SMART Sockets & Plugs, and SMART thermostats.
Powering consumes House Electricity.
Providing is informatical and systemic.
Energy Manager & Interface handles Providing.
Providing yields Control Suggestions.
Transmitting is informatical and systemic.
Transmitting consumes Price.
Transmitting yields Energy Manager & Interface

Figures of Merit (FOM)

Electricity consumption varies by time of day, depending on social practices of consumers. This often does not correspond well to the availability of energy, since most renewable sources exhibit variable supply. The diagram below, by the US Energy Information Administration, shows the degree of variability of energy consumption by time of day and month of year. Thus, one of our figures of merit is load reduction during peak hours [%].

The primary use case of dynamic pricing is for demand reduction during peak consumption periods, to be able to match available capacity with demand. Although there are a few smart grid systems with dynamic pricing, the availability of data with respect to demand response is sparse. However, there are a number of pilot studies in the academic literature that demonstrate favorable reductions in peak demand with dynamic pricing.

This plot is from a survey paper by Faruqui et. al where terms are defined as follows:

• TOU: time of use pricing
• TOU w/ Tech: time of use pricing with technology enablers
• PTR: peak time rebate
• CPP: critical peak pricing
• CPP w/ Tech: critical peak pricing with technology enablers
• RTP: real time pricing
• RTP w/ Tech: real time pricing with technology enablers

The conclusion is that the efficacy of dynamic pricing in achieving the peak demand reduction FOM depends on the particular implementation of the scheme. Our technology would implement a combination of TOU + CPP and RTP systems, and would be enabled both by in-home sensor technologies as well as user-facing applications.

The theoretical limit of peak demand reduction depends on location, so that demand always matches the supply of energy, which is optimally produced by a mix of renewable and nonrenewable sources. Unfortunately, the technology is in its infancy, and all of the pilot studies were done in the first decade of the 20th century, so it is difficult to discern any trends. However, we would assume that improved wireless communication, cloud data storage, machine learning and better sensing over the last 10 years can only improve the efficacy of dynamic pricing in peak demand reduction.