Investment Optimization to Improve Power System Resilience

Simultaneous process synthesis, heat, power, and water integration of thermochemical hybrid biomass, coal, and natural gas facilities

Guest Editorial: Situational awareness of integrated energy systems

To integrate the location, inventory, and routing (LIR) problems arising in designing a resilient sustainable perishable food supply network (RSPFSN), a bi-objective optimization model is developed. To improve the resiliency and sustainability of the RSPFSN, a dynamic pricing strategy is used to cope with the disrupting events, along with minimizing the total cost and CO2 emission of the whole network. One of the important features of the proposed model is taking into account the effects of route disruptions and traffic conditions on the deterioration of products. To solve the mixed-integer nonlinear bi-objective optimization model, a novel hybrid method is developed using the Heuristic Multi-Choice Goal Programming and Utility Function Genetics Algorithm (HMCGP-UFGA). To improve resiliency, the dynamic pricing strategy, considering the traffic condition, can lead to around a 20% improvement in both cost and CO2 emission, based on the results of our case study in a dairy supply chain. Besides, the results of sensitivity analysis display the high flexibility of the proposed approach for various problems.

Power systems are susceptible to natural threats including hurricanes and\nfloods. Modern power grids are also increasingly threatened by cyber attacks.\nExisting approaches that help improve power system security and resilience may\nnot be sufficient; this is evidenced by the continued challenge to supply\nenergy to all customers during severe events. This paper presents an approach\nto address this challenge through bio-inspired power system network design to\nimprove system reliability and resilience against disturbances. Inspired by\nnaturally robust ecosystems, this paper considers the optimal ecological\nrobustness that recognizes a unique balance between pathway efficiency and\nredundancy to ensure the survivability against disruptive events for given\nnetworks. This paper presents an approach that maximizes ecological robustness\nin transmission network design by formulating a mixed-integer nonlinear\nprogramming optimization problem with power system constraints. The results\nshow the increase of the optimized power system's robustness and the improved\nreliability with less violations under N-x contingencies.\n

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.

Loading margin calculation with line switching: A decomposition method

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

A probability constrained multi-objective optimization model for CCHP system operation decision support

Shared Energy Storage System (ESS) plays a crucial role in addressing distributed Photovoltaic (PV) curtailment and mitigating distribution network investment costs. To this end, this paper proposes an optimal configuration strategy for shared ESS that considers both the equivalent modeling of distributed PV clusters and distribution network reconfiguration. Firstly, a distributed PV cluster equivalent model was developed, which simultaneously considers electrical distance and active power balance, to facilitate the clustering and equivalencing of distributed PV systems. Secondly, an optimization model for the allocation of shared ESS, considering network reconfiguration and PV clustering results, is proposed. This model minimizes investment costs and maximizes PV energy consumption, while comprehensively considering operational constraints related to PV, ESS, and distribution network reconfiguration. Thirdly, to address the challenge of model solving, a chaotic neural network algorithm is developed by integrating chaotic local search and quasi-oppositional-based learning methods to solve the proposed mixed-integer nonlinear optimization model. Finally, the effectiveness of the proposed method is demonstrated through several case studies based on the IEEE test systems, and the obtained Pareto optimal frontier can provide effective guidance for investors.

In recent years, frequent occurrence of extreme weather events has caused great losses to the power system. Meanwhile, the high penetration of renewable energy has brought new challenges to the safe operation of the system. Coordination of various measures against different stages of disaster can effectively reduce the overall loss and enhance system's resilience. This paper proposed a methodology to coordinate preventive and emergency dispatch stages based on the defender–attacker–defender model, in which various dispatching measures are modelled as defenders, whereas the extreme weather event and wind power's uncertainty are modelled as attackers. Specifically, a two‐stage three‐layer robust optimal dispatching model is established, in which conventional generator's output, wind farm's grid‐connected planning, and load shedding are taken as the main dispatching measures. To solve this model, the column and constraint generation algorithm is used to transform the three‐layer optimization model into a mixed integer linear problem . The effectiveness of the proposed model is verified in the modified IEEE RTS‐79 test system, and the results show that the coordination of preventive and emergency dispatch can effectively reduce the load outage cost under extreme weather, thus enhancing the ability of power system to cope with extreme scenarios.

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Water rights management modeling was considered the useful measure to ensure efficient water resources allocation and to avoid intensifying conflicts over water rights. Regarding to the two major patterns of water rights across the world, namely, quantitative water rights (QWR) and duration-based water rights (DWR), the corresponding measures are fastidious. Since QWR was established to set water entitlements instead of DWR in Heihe River Basin, China, the disability of usual DWR tool, managing water diversion by water intakes closing (WIC) during a certain period, has arisen increasingly. The historical data also reflected the low reliability of DWR in allocating water resources quantitatively without modeling guidelines. Aimed at the challenge, a modeling framework and a mixed integer optimization model (MIOM) combing QWR objectives and DWR tools were developed, by which the optimal schemes in Pareto frontier were found to improve the water intake on/off events, including WIC stages, duration days, and starting date through long term series probability analysis and historical data. Results showed that the reliability of DWR allocation was enhanced and WIC duration was reduced significantly compared with current regulation. Scenario analyses indicated that MIOM was powerful for assisting DWR tool to manage QWR and the proposed framework was able to manage water rights efficiently in comparable basins.

Systems are damaged due to various disturbances, and the reliability of the systems is reduced. Measures to improve system resilience need to be studied since many systems still need to operate normally after suffering damage. In this paper, the whole process of the disturbance and recovery of the system is considered, and a resilience optimization model of a multi-component system is proposed. Firstly, a system resilience assessment method is proposed based on system reliability, and the system resilience loss is used as the resilience assessment index. Secondly, two component importance indexes, loss importance and recovery importance, are proposed for the system disturbance phase and recovery phase, respectively. The two importance indexes are weighted to obtain the weighted importance so as to measure the change law of system resilience and determine the influence degrees of components on system reliability. Then, under the constraint of maintenance time, an optimization model is established to determine a redundancy strategy to maximize system resilience. Finally, through an example analysis of a wind turbine system with its main components, it is verified that the redundancy strategy proposed with this method can reduce the loss of system resilience and effectively improve system reliability.

The cybersecurity of electric power grids is emerging as a critical challenge for the power industry in the transformation of modern power systems towards the future smart grid. It is of great importance to enhance the resilience of power systems against potential cyber threats. In this paper, a distributionally robust recovery resource allocation method based on the tri-level defender-attacker-defender (D-A-D) model is proposed to enhance the resilience of power systems in the face of malicious cyberattacks. The proposed recovery resource allocation method is able to optimally distribute the recovery resources among the substations in the grid to mitigate the impacts of successful cyberattacks during the recovery process, and improve the resilience performance of power systems against the attacks. In the proposed model, the recovery resource represents the available resource that can be expended to support the recovery efforts of the cyber-physical power system. It is expected to accelerate the recovery process of the system after successful attacks. The recovery resource may include necessary software, hardware, and labor force. Meanwhile, a distributionally robust optimization (DRO) model is proposed to address the uncertainty of the power system operation conditions, e.g., renewable energy resources (RESs). In order to verify the proposed system defense method, case studies were conducted on the IEEE Reliability Test System RTS-79. The results of the case studies show that the proposed method can provide a robust solution to recovery resource allocation for mitigating the risk of potential cyberattacks.

Optimal resilient operation of hub energy system considering uncertain parameters