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1. Uncertainty Cost of Stochastic Producers: Metrics and Impacts on Power Grid Flexibility

   https://doi.org/10.1109/TEM.2020.2970729  

   Pourahmadi, F. ; Hosseini, S.H. ; Dehghanian, P. ; Shittu, E. ; Fotuhi-Firuzabad, M. ( February 2021 , IEEE transactions on engineering management)

   null (Ed.)

   The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques. 

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2. Stochastic Planning of a Mostly-Renewable Power Grid

   https://doi.org/10.1109/CCTA54093.2023.10253007  

   Chen, Sunny ; Mathieu, Johanna L. ; Seiler, Peter ( August 2023 , IEEE Conference on Control Technology and Applications (CCTA))

   Power grid resource adequacy can be difficult to ensure with high penetrations of intermittent renewable energy. We explore enhancing resource adequacy by overbuilding renewables while modeling statistical correlations in renewable power at different sites. Overbuilding allows production during times of low power, and exploiting statistical correlations can reduce power variability and, subsequently, reduce needed renewable capacity. In this work, we present a stochastic optimization problem to size renewables and expand transmission while minimizing the expected dispatch cost. Our method uses statistical profiles of renewable production and embeds network constraints using the DC power flow equations. We assess our method’s effects on feasibility, load shedding, locational marginal prices, and generator curtailment. On the IEEE 9-bus system, we found that anti-correlation between generators reduced generation capacity needs with sufficient transmission. On the IEEE 30-bus system, we found that the optimal solution required significant overbuilding and curtailment of renewables regardless of the marginal cost of schedulable generation. 

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3. Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

   https://doi.org/10.1109/TII.2022.3195695  

   Alhazmi, Mohannad ; Dehghanian, Payman ; Nazemi, Mostafa ; Oikonomou, Konstantinos ( January 2022 , IEEE Transactions on Industrial Informatics)

   The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. The closely-intertwined ecosystem of water and power infrastructures is commonly known as water-energy nexus. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a Boolean solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks. 

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4. Short-Term Transmission Line Maintenance Scheduling with Wind Energy Integration,

   Wang, Chong ; Wang, Zhaoyu ( October 2017 , IEEE PES General Meeting)

   Transmission line maintenance scheduling (TLMS) plays an important role in enhancement of component reliability. When conducting short-term TLMS, system operators should consider not only operating costs but also operating constraints, particularly with increasing integration of large-scale wind generation. This paper proposes a stochastic security-constrained model to establish short-term TLMS in consideration of wind generation. Possible scenarios, generated by the Latin hypercube sampling (LHS) technique, are simulated to represent wind power volatility. For each line to be maintained during the maintenance windows, Kirchoff’s law is enforced by using a big-M formulation. Unit commitment is also considered to coordinate TLMS to achieve the best maintenance strategies. The whole problem is modeled as a mixed integer linear programming problem, which is solved by the CPLEX solver. Numerical tests on a six-bus system and a modified IEEE 118-bus system show the effectiveness of the proposed model and the algorithm. 

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5. Risk-limiting Unit Commitment in Smart Grid with Intelligent Periphery

   https://doi.org/10.1109/TPWRS.2017.2672939  

   Peng, Chaoyi ; Hou, Yunhe ; Yu, Nanpeng ; Wang, Weisheng ( February 2017 , IEEE Transactions on Power Systems)

   This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multi-stage multi-period unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a 9-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP. 

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