6,839 research outputs found
A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids
We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy
Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids
In this paper, we propose and study the effectiveness of customer engagement
plans that clearly specify the amount of intervention in customer's load
settings by the grid operator for peak load reduction. We suggest two different
types of plans, including Constant Deviation Plans (CDPs) and Proportional
Deviation Plans (PDPs). We define an adjustable reference temperature for both
CDPs and PDPs to limit the output temperature of each thermostat load and to
control the number of devices eligible to participate in Demand Response
Program (DRP). We model thermostat loads as power throttling devices and design
algorithms to evaluate the impact of power throttling states and plan
parameters on peak load reduction. Based on the simulation results, we
recommend PDPs to the customers of a residential community with variable
thermostat set point preferences, while CDPs are suitable for customers with
similar thermostat set point preferences. If thermostat loads have multiple
power throttling states, customer engagement plans with less temperature
deviations from thermostat set points are recommended. Contrary to classical
ON/OFF control, higher temperature deviations are required to achieve similar
amount of peak load reduction. Several other interesting tradeoffs and useful
guidelines for designing mutually beneficial incentives for both the grid
operator and customers can also be identified
Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach
This paper studies a three party energy management problem in a user
interactive smart community that consists of a large number of residential
units (RUs) with distributed energy resources (DERs), a shared facility
controller (SFC) and the main grid. A Stackelberg game is formulated to benefit
both the SFC and RUs, in terms of incurred cost and achieved utility
respectively, from their energy trading with each other and the grid. The
properties of the game are studied and it is shown that there exists a unique
Stackelberg equilibrium (SE). A novel algorithm is proposed that can be
implemented in a distributed fashion by both RUs and the SFC to reach the SE.
The convergence of the algorithm is also proven, and shown to always reach the
SE. Numerical examples are used to assess the properties and effectiveness of
the proposed scheme.Comment: 6 pages, 4 figure
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