A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice

Analysis of Solar Energy Aggregation under Various Billing Mechanisms

Ongoing reductions in the cost of solar photovoltaic (PV) systems are driving their increased installations by residential households. Various incentive programs such as feed-in tariff, net metering, net purchase and sale that allow the prosumers to sell their generated electricity to the grid are also powering this trend. In this paper, we investigate sharing of PV systems among a community of households, who can also benefit further by pooling their production. Using cooperative game theory, we find conditions under which such sharing decreases their net total cost. We also develop allocation rules such that the joint net electricity consumption cost is allocated to the participants. These cost allocations are based on the cost causation principle. The allocations also satisfy the standalone cost principle and promote PV solar aggregation. We also perform a comparative analytical study on the benefit of sharing under the mechanisms favorable for sharing, namely net metering, and net purchase and sale. The results are illustrated in a case study using real consumption data from a residential community in Austin, Texas.Comment: 12 page

On-line job-shop scheduling of a manufacturing system based on a virtual supervisor concept

The controls for reconfigurable manufacturing systems have to be capable not only of identifying exceptions on-line, but also simultaneously developing on-line strategies for unpredictable customer orders or inaccurate estimates of processing times. This paper presents an approach for job-shop scheduling with uncertain arrival times. The approach exploits Virtual Supervisor (VS) concept, which provides access to all system information during program execution and thus can readily monitor the overall system performance. The goal is to minimize expected part tardiness and earliness cost. A solution methodology based on a combined Lagrangian relaxation, VS-Patterns, Maxwell equations and temporal difference is developed to obtain a dual solution for on-line implementation

On-line scheduling method of manufacturing system based on VS algorithm for reference pattern

In this paper, a scheduling method is developed provide planning for manufacturing plants with multiple coordinating cells. The controls for reconfigurable manufacturing systems have to be capable not only of identifying exceptions on-line, but also simultaneously developing on-line strategies for unpredictable customer order changes or inaccurate estimate of processing times. The approach exploits virtual supervisor (VS) concept developing an algorithm which provides access to all system information during program execution and thus can readily monitor the overall system performance creating reference pattern. The goal is to minimize expected costs of part tardiness and/or earliness. A solution methodology based on a combined Lagrangian relaxation, VS-Patterns, Maxwell equations and temporal difference is developed to reduce the computational requirements for large problems. Sequences pattern shows that near optimal schedules can be obtained a dual solution for on-line implementation

Identification of Wiener Models based on SVM and Orthonormal Bases

In this paper, a novel method for the identification of the linear and nonlinear blocks in a Wiener model is presented. The method combines Support Vector Machines and Least Squares Prediction Error techniques. The identification is carried out by minimizing an augmented cost function defined as the sum of the standard structural risk function appearing in Support Vector Regression and the quadratic criterion on the prediction errors associated to Least Squares estimation methods. The properties of the proposed method are illustrated through simulation examples.Sociedad Argentina de Informática e Investigación Operativ

Linear Minimax Estimation for Random Vectors with Parametric Uncertainty

Abstract-In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t)