Payman Dehghanian

Improving the coordinated operation of energy and water resources is essential for agricultural parks. However, short-term source-load prediction errors are considerable, which often forces uncertainty-based optimization methods to be overly conservative. Moreover, demand-side flexible resources remain underexploited. To tackle these problems, this paper proposes a two-stage distributionally robust optimization (DRO) scheduling strategy. The strategy integrates two key components: an improved incentive-based integrated demand response (IBIDR) covering electricity, heat, and water, and a data-driven ellipsoidal uncertainty set constructed from CNN-Transformer-MTL prediction errors. The main work is threefold. First, a CNN-Transformer-MTL prediction model is developed to improve the prediction accuracy of PV power and heat load. An ellipsoidal uncertainty set is then constructed based on the prediction errors, which significantly reduces the conservativeness of the set. Second, the improved IBIDR is designed for electricity, heat, and water loads. It combines stepped subsidies for adjustable loads with substitution-based response of replaceable loads. Third, four models are compared: a deterministic model, a DRO model without demand response, a DRO model with price-based integrated demand response, and the proposed improved IBIDR-DRO model. Case study results are as follows. The CNN-Transformer-MTL model achieves the best prediction performance for both PV power and heat load. The resulting uncertainty set has the smallest area: it is up to 24.64% smaller than those built from the errors of other prediction models, and 27.3% smaller than the box set at the same confidence level. This effectively alleviates the over-conservatism of DRO. Meanwhile, the proposed model reduces the total operating cost by 10.4% compared with the DRO model, and by 7.1% compared with the DRO model with price-based integrated demand response. In particular, the flexible scheduling of pumps and water storage facilities cuts the peak pump load by 23%, confirming the role of the water subsystem as a flexibility provider. These results demonstrate that integrating data-driven uncertainty characterization with incentive-based demand response is an effective way to unlock the flexibility of energy-water nexus systems.

As renewable energy penetration continues to increase, virtual power plant (VPP) plays an important role in coordinating distributed resources and supporting low carbon power system operation. This paper proposes a bi-level optimal scheduling framework integrating generation-side and user-side carbon credit mechanisms, carbon credit feedback and differentiated demand response (DR), with the carbon credit price serving as a key factor for carbon credit valuation and DR pricing. The upper level minimizes the VPP's operating cost by coordinating wind power (WP), photovoltaic (PV) generation, the gas turbine (GT) operation, the energy storage system (ESS) operation, and power exchanges with main grid, while considering carbon credit transactions, DR compensation and the user-side feedback penalties. The lower level minimizes user electricity costs by renewable electricity allocation and carbon credit accounts incorporating cumulative credits, dynamic thresholds, subsidies, revenues and threshold exceedance costs. The carbon credit price adjusts the DR price according to each user’s threshold exceedance, supporting differentiated load curtailment and shifting. The two levels are coupled through user loads adjusted by DR and carbon credit threshold exceedance. Case study shows that coordinating carbon credits with DR reduces the total VPP operating cost by 33.41%, the GT operating cost by 44.60% and the total user-side electricity cost by 28.67% compared with baseline economic dispatch.

Malicious and negligent insiders pose significant security risks to mission‐critical systems like electric power grids due to their high privileges in increasingly digitized infrastructures. This paper investigates the vulnerability of smart power grids to load redistribution (LR) attacks in the presence of insider threats. We introduce a stochastic optimization model to minimize the expected risk of high operation costs due to LR attacks by protecting critical grid components and deploying detection technologies, such as honeypots, to detect insider threats and prevent information leakage. Our model accounts for uncertainties in insider presence, honeypot effectiveness, and attack targets, and uses the conditional value‐at‐risk (CVaR) measure, which can be adjusted based on the decision‐maker's conservatism. In addition, it accounts for real‐time power demand variations and dynamic false‐data injection by attackers. To enhance tractability, we transform our model, originally formulated as a trilevel mixed‐integer nonlinear programming (Tri‐MINLP) problem, into an approximate single‐level mixed‐integer linear programming (MILP) formulation. We apply our proposed model to the IEEE 14‐bus test system, and our results highlight the effectiveness of our approach in lowering the risk of high operation costs due to LR attacks. In addition, we present several insights by assessing the impact of key factors on the expected financial risk of attacks, including the protection budget, insider‐threat likelihood, honeypot‐detection effectiveness, and the defender's decision‐making conservatism.