Abstract:Abstract-We consider the problem of forecasting the aggregate demand of a pool of price-responsive consumers of electricity. The price-response of the aggregation is modeled by an optimization problem that is characterized by a set of marginal utility curves and minimum and maximum power consumption limits. The task of estimating these parameters is addressed using a generalized inverse optimization scheme that, in turn, requires solving a nonconvex mathematical program. We introduce a solution method that ove… Show more
“…In this setting, each market participant solves an optimization problem to determine a bid which they submit to a facilitator, who in turn incorporates the bids in a market clearing optimization problem that determines prices and the consumption or production allocated to each bidder. In this context, the facilitator may impute bidders' right-hand-side constraint parameters, such as bounds on consumption, which can then be used to inform a pricing strategy that aims to maximize profit or control peak demand (Saez-Gallego et al, 2016;Saez-Gallego & Morales, 2018;Xu et al, 2018;Lu et al, 2019). Similarly, a bidder may seek to impute several unknown parameters which can be used in the process of deciding her bid.…”
Most inverse optimization models impute unspecified parameters of an objective function to make an observed solution optimal for a given optimization problem with a fixed feasible set. We propose two approaches to impute unspecified left-hand-side constraint coefficients in addition to a cost vector for a given linear optimization problem. The first approach identifies parameters minimizing the duality gap, while the second minimally perturbs prior estimates of the unspecified parameters to satisfy strong duality, if it is possible to satisfy the optimality conditions exactly. We apply these two approaches to the general linear optimization problem. We also use them to impute unspecified parameters of the uncertainty set for robust linear optimization problems under interval and cardinality constrained uncertainty. Each inverse optimization model we propose is nonconvex, but we show that a globally optimal solution can be obtained either in closed form or by solving a linear number of linear or convex optimization problems.
“…In this setting, each market participant solves an optimization problem to determine a bid which they submit to a facilitator, who in turn incorporates the bids in a market clearing optimization problem that determines prices and the consumption or production allocated to each bidder. In this context, the facilitator may impute bidders' right-hand-side constraint parameters, such as bounds on consumption, which can then be used to inform a pricing strategy that aims to maximize profit or control peak demand (Saez-Gallego et al, 2016;Saez-Gallego & Morales, 2018;Xu et al, 2018;Lu et al, 2019). Similarly, a bidder may seek to impute several unknown parameters which can be used in the process of deciding her bid.…”
Most inverse optimization models impute unspecified parameters of an objective function to make an observed solution optimal for a given optimization problem with a fixed feasible set. We propose two approaches to impute unspecified left-hand-side constraint coefficients in addition to a cost vector for a given linear optimization problem. The first approach identifies parameters minimizing the duality gap, while the second minimally perturbs prior estimates of the unspecified parameters to satisfy strong duality, if it is possible to satisfy the optimality conditions exactly. We apply these two approaches to the general linear optimization problem. We also use them to impute unspecified parameters of the uncertainty set for robust linear optimization problems under interval and cardinality constrained uncertainty. Each inverse optimization model we propose is nonconvex, but we show that a globally optimal solution can be obtained either in closed form or by solving a linear number of linear or convex optimization problems.
“…The formulation of an inverse problem for linear programming in [9] took a slightly more general perspective, as it does not assume that the given data necessarily arises as the solution of a linear program, and rather seeks to minimize the distance to the solution set of a linear program. Recent application of the inverse problem for linear programming may be found in [24], for example. These papers on inverse linear programming are foundational and have opened up a great deal of subsequent research.…”
Discrete optimal transportation problems arise in various contexts in engineering, the sciences and the social sciences. Often the underlying cost criterion is unknown, or only partly known, and the observed optimal solutions are corrupted by noise. In this paper we propose a systematic approach to infer unknown costs from noisy observations of optimal transportation plans. The algorithm requires only the ability to solve the forward optimal transport problem, which is a linear program, and to generate random numbers. It has a Bayesian interpretation, and may also be viewed as a form of stochastic optimization.We illustrate the developed methodologies using the example of international migration flows. Reported migration flow data captures (noisily) the number of individuals moving from one country to another in a given period of time. It can be interpreted as a noisy observation of an optimal transportation map, with costs related to the geographical position of countries. We use a graph-based formulation of the problem, with countries at the nodes of graphs and non-zero weighted adjacencies only on edges between countries which share a border. We use the proposed algorithm to estimate the weights, which represent cost of transition, and to quantify uncertainty in these weights.
“…Generally, the price-responsiveness of a consumer depends on various factors, e.g., weather conditions, electricity price, time and type of day, and season, etc. [33]. As an example, in [34], it is shown that the response of the load demand has been faster in the cold weather.…”
Demand flexibility will be an inevitable part of the future power system operation to compensate stochastic variations of ever-increasing renewable generation. One way to achieve demand flexibility is to provide time-varying prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services (AS) provision. The proposed method should account for rebound effect and variability of the customers' reaction to the price signals. In this study, an efficient algorithm is developed for consumers' flexibility estimation by the transmission system operator (TSO) based on offline data. No aggregator or real-time communication is involved in the process of flexibility estimation, although realtime communication channels are needed to broadcast price signals to the end-users. Also, the consumers' elasticity and technical differences between various types of loads are taken into account in the formulation. The problem is formulated as a mixed-integer linear programming (MILP) problem, which is then converted to a chance-constrained programming to account for the stochastic behaviour of the consumers. Simulation results show the applicability of the proposed method for the provision of AS from consumers at the TSO level.
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