Predictability and Fairness in Load Aggregation and Operations of
Virtual Power Plants
- URL: http://arxiv.org/abs/2110.03001v1
- Date: Wed, 6 Oct 2021 18:20:07 GMT
- Title: Predictability and Fairness in Load Aggregation and Operations of
Virtual Power Plants
- Authors: Jakub Marecek, Michal Roubalik, Ramen Ghosh, Robert N. Shorten, Fabian
R. Wirth
- Abstract summary: In power systems, one wishes to regulate the aggregate demand of an ensemble of distributed energy resources.
We suggest a notion of predictability and fairness, which suggests that the long-term averages of prices or incentives offered should be independent of the initial states of the operators of the DER.
- Score: 3.8113588584597187
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In power systems, one wishes to regulate the aggregate demand of an ensemble
of distributed energy resources (DERs), such as controllable loads and battery
energy storage systems. We suggest a notion of predictability and fairness,
which suggests that the long-term averages of prices or incentives offered
should be independent of the initial states of the operators of the DER, the
aggregator, and the power grid. We show that this notion cannot be guaranteed
with many traditional controllers used by the load aggregator, including the
usual proportional-integral (PI) controller. We show that even considering the
non-linearity of the alternating-current model, this notion of predictability
and fairness can be guaranteed for incrementally input-to-state stable (iISS)
controllers, under mild assumptions.
Related papers
- Predictability and Fairness in Load Aggregation with Deadband [0.0]
We consider the effects of losses in the alternating current model and the deadband in the controller.
We show that Filippov invariant measures enable reasoning about predictability and fairness.
arXiv Detail & Related papers (2023-05-28T13:50:05Z) - Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - Evaluating the Planning and Operational Resilience of Electrical
Distribution Systems with Distributed Energy Resources using Complex Network
Theory [0.0]
This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events.
The proposed framework is developed by effectively employing the complex network theory.
arXiv Detail & Related papers (2022-08-24T13:41:37Z) - Privacy-preserving household load forecasting based on non-intrusive
load monitoring: A federated deep learning approach [3.0584272247900577]
We first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM)
The integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model.
arXiv Detail & Related papers (2022-06-30T11:13:26Z) - Lyapunov Density Models: Constraining Distribution Shift in
Learning-Based Control [64.61499213110334]
We seek a mechanism to constrain the agent to states and actions that resemble those that it was trained on.
In control theory, Lyapunov stability and control-invariant sets allow us to make guarantees about controllers.
density models allow us to estimate the training data distribution.
arXiv Detail & Related papers (2022-06-21T16:49:09Z) - Sparsity in Partially Controllable Linear Systems [56.142264865866636]
We study partially controllable linear dynamical systems specified by an underlying sparsity pattern.
Our results characterize those state variables which are irrelevant for optimal control.
arXiv Detail & Related papers (2021-10-12T16:41:47Z) - Prescribing net demand for two-stage electricity generation scheduling [0.0]
We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch.
Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation.
We propose a bilevel program to construct a prescription of the net demand that does account for the power system's cost asymmetry.
arXiv Detail & Related papers (2021-08-02T16:05:53Z) - Provably Correct Controller Synthesis of Switched Stochastic Systems
with Metric Temporal Logic Specifications: A Case Study on Power Systems [9.191903314933915]
We present a provably correct controller synthesis approach for switched control systems with metric logic (MTL) specifications with provable probabilistic guarantees.
We first present the control bisimulation function for switched control systems, which bounds trajectory divergence between the switched control system and its nominal deterministic control system.
We then develop a method to compute optimal control inputs by solving an optimization problem for the nominal trajectory of the deterministic control system.
arXiv Detail & Related papers (2021-03-26T04:50:29Z) - Probabilistic electric load forecasting through Bayesian Mixture Density
Networks [70.50488907591463]
Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids.
We propose a novel PLF approach, framed on Bayesian Mixture Density Networks.
To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
arXiv Detail & Related papers (2020-12-23T16:21:34Z) - Physics-Informed Gaussian Process Regression for Probabilistic States
Estimation and Forecasting in Power Grids [67.72249211312723]
Real-time state estimation and forecasting is critical for efficient operation of power grids.
PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system.
We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
arXiv Detail & Related papers (2020-10-09T14:18:31Z) - Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable
Edge Computing Systems [87.4519172058185]
An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied.
A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem.
Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
arXiv Detail & Related papers (2020-02-20T04:58:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.