Although distribution grid customers are obliged to share their consumption data with system operators (DSOs), a possible leakage of this is often disregarded in operational routines DSOs. This paper introduces privacy-preserving optimal power flow (OPF) mechanism for grids that secures customer privacy from unauthorised access OPF solutions, e.g., current and voltage measurements. The based on the framework differential allows control participation risks individuals dataset by applying...

This paper deals with the problem of clearing sequential electricity markets under uncertainty. We consider European approach, where reserves are traded separately from energy to meet exogenous reserve requirements. Recently, proposed stochastic dispatch models that co-optimize these services provide most efficient solution in terms expected operating costs by computing needs endogenously. However, incompatible existing market designs. proposes a new method compute requirements bring outcome...

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share due privacy risks. To control these risks, we develop privacy-preserving algorithms for synthetic generation datasets optimization and machine learning. Taking a dataset as input, output its noisy, version, which preserves accuracy real specific downstream model or even large population those. We loss using Laplace Exponential mechanisms...

Distributed algorithms enable private Optimal Power Flow (OPF) computations by avoiding the need in sharing sensitive information localized sub-problems. However, adversaries can still infer this from coordination signals exchanged across iterations. This paper seeks formal privacy guarantees for distributed OPF and provides differentially based on consensus Alternating Direction Method of Multipliers (ADMM). The proposed attain differential introducing static dynamic random perturbations...

This paper addresses a multi-stage generation investment problem for strategic (price-maker) power producer in electricity markets. is exposed to different sources of uncertainty, including short-term operational (e.g., rivals' offering strategies) and long-term macro demand growth) uncertainties. formulated as stochastic bilevel optimization problem, which eventually recasts large-scale mixed-integer linear programming (MILP) with limited computational tractability. To cope issues, we...

We propose stochastic control policies to cope with uncertain and variable gas extractions in natural networks. Given historical extraction data, these are optimized produce the real-time inputs for nodal injections pressure regulation rates by compressors valves. describe random network state as a function of inputs, which enables chance-constrained optimization arbitrary topologies. This ensures flow feasibility minimal variation up specified variance criteria. Furthermore, provides...

Demand response is considered an effective mechanism to deal with the intermittency and stochasticity of renewable sources in power systems. These mechanisms assume that participants behave as rational decision-makers. However, behavioural economists commonly agree decision-makers are rather bounded deviate from optimal response. The purpose this paper generalize existing account for rationality. This done framework coupon incentive-based demand program, by formulating a Stackelberg game...

While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how propagate into prices, revealing notable pricing and their spatial disparity in congested power systems. To improve fairness, we propose to embed market-clearing optimization as a layer. Differentiating through this layer allows for balancing between errors, oppose minimizing alone. implicitly optimizes fairness controls the...

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments dispatch decisions. This paper jointly studies the impact of choices on investment in electrolyzers capacity. We present a bilevel model that incorporates infrastructure, day-ahead dispatch, potential redispatch actions near real-time ensure constraints respected. Our findings...

Renewable power producers participating in electricity markets build forecasting models independently, relying on their own data, model and feature preferences. In this paper, we argue that renewable-dominated markets, such an uncoordinated approach to results substantial opportunity costs for stochastic additional operating the system. As a solution, introduce Regression Equilibrium--a welfare-optimal state of under uncertainty, where profit-seeking do not benefit by unilaterally deviating...

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at scheduling stage thus necessary for system operators proactively optimize power flows, manage congestion, and reduce cost grid operations. However, remains challenging due uncertainty. To reliably predict DLRs, we propose new probabilistic forecasting model graph convolutional LSTM. Like standard LSTM...

We develop multi-stage linear decision rules (LDRs) for dynamic power system generation and energy storage investment planning under uncertainty propose their chance-constrained optimization with performance guarantees. First, the optimized LDRs guarantee operational carbon policy feasibility of resulting plan even when distribution is ambiguous. Second, internalize tolerance planner towards stochasticity (variance) uncertain outcomes. They can eventually produce a quasi-deterministic plan,...

Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated scalable tools to achieve market efficiency. To account for imbalance costs real-time while remaining compatible with existing sequential market-clearing structure, our work adopts an uncertainty-informed adjustment toward VRES contract quantity scheduled day-ahead market. This mechanism solving a bilevel problem, which is computationally challenging practical large-scale...

This work proposes an uncertainty-informed bid adjustment framework for integrating variable renewable energy sources (VRES) into electricity markets. adopts a bilevel model to compute the optimal VRES day-ahead bids. It aims minimize expected system cost across and real-time stages approximate efficiency of stochastic market design. However, solving optimization problem is computationally challenging large-scale systems. To overcome this challenge, we introduce novel technique based on...