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Most of the current research on operating reserve markets assumes that the required capacity is predetermined, which means that the demand of operating reserve capacity is inelastic. This paper considers that operating reserve capacity in a power system is flexible and that one should optimize it by cost-benefit analysis. Based on the reliability evaluation of the generation system, a clearing model of the operating reserve market is proposed to determine the optimal reserve capacity and simultaneously clear the operating reserve market. The model is discussed on both uniform-price and pay-as-bid auction mechanisms. By using a heuristic method, we suggest an efficient algorithm to solve the model. Case studies for reliability test system (RTS96) demonstrate the usefulness and efficiency of the proposed model.Index Terms-Cost-benefit analysis, operating reserve capacity, power market, reliability.

Abstract-In this paper, we study unit commitment (UC) problems considering the uncertainty of load and wind power generation. UC problem is formulated as a chance-constrained two-stage stochastic programming problem where the chance constraint is used to restrict the probability of load imbalance. In addition to the conventional mixed integer linear programming formulation using Big-M, we present the bilinear mixed integer formulation of chance constraint, and then derive its linear counterpart using McCormick linearization method. Then, we develop a bilinear variant of Benders decomposition method, which is an easy-to-implement algorithm, to solve the resulting large-scale linear counterpart. Our results on typical IEEE systems demonstrate that (i) the bilinear mixed integer programming formulation is stronger than the conventional one; (ii) the proposed Benders decomposition algorithm is generally an order of magnitude faster than using a professional solver to directly compute both linear and bilinear chance-constrained UC models.

Antibiotic resistance is now a major threat to human health, and one approach to combating this threat is to develop resistance-resistant antibiotics. Synthetic antimicrobial polymers are generally resistance resistant, having good activity with low resistance rates but usually with low therapeutic indices. Here, we report our solution to this problem by introducing dual-selective mechanisms of action to a short amidine-rich polymer, which can simultaneously disrupt bacterial membranes and bind to bacterial DNA. The oligoamidine shows unobservable resistance generation but high therapeutic indices against many bacterial types, such as ESKAPE strains and clinical isolates resistant to multiple drugs, including colistin. The oligomer exhibited excellent effectiveness in various model systems, killing extracellular or intracellular bacteria in the presence of mammalian cells, removing all bacteria from Caenorhabditis elegans, and rescuing mice with severe infections. This “dual mechanisms of action” approach may be a general strategy for future development of antimicrobial polymers.

This paper presents a chance constrained information gap decision model for multi-period microgrid expansion planning (MMEP) considering two categories of uncertainties, namely random and non-random uncertainties. The main task of MMEP is to determine the optimal sizing, type selection, and installation time of distributed energy resources (DER) in microgrid. In the proposed formulation, information gap decision theory (IGDT) is applied to hedge against non-random uncertainties of long-term demand growth. Then, chance constraints are imposed in the operational stage to address the random uncertainties of hourly renewable energy generation and load variation. The objective of chance constrained information gap decision model is to maximize the robustness level of DER investment meanwhile satisfying a set of operational constraints with a high probability. The integration of IGDT and chance constrained program, however, makes it very challenging to compute. To address this challenge, we propose and implement a strengthened bilinear Benders decomposition method. Finally, the effectiveness of proposed planning model is verified through the numerical studies on both the simple and practical complex microgrid. Also, our new computational method demonstrates a superior solution capacity and scalability. Compared to directly using a professional mixed integer programming solver, it could reduce the computational time by orders of magnitude.

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