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Register a new userA Mathematical Approach to Improve Energy-Water Nexus Reliability Using a Novel Multi-Stage Adjustable Fuzzy Robust Approach
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A system of a systems approach that analyzes energy and water systems simultaneously is called energy-water nexus. Neglecting the interrelationship between energy and water drives vulnerabilities whereby limits on one resource can cause constraints on the other resource. Power plant energy production directly depends on water availability, and an outage of the power systems will affect the wastewater treatment facility processes. Therefore, it is essential to integrate energy and water planning models. As mathematical energy-water nexus problems are complex, involve many uncertain parameters, and are large-scale, we proposed a novel multi-stage adjustable Fuzzy robust approach that balances the solutions robustness against the budget-constraints. Scenario-based analysis indicates that the proposed approach generates flexible and robust decisions that avoid excessive costs compared to conservative methods. Keywords: Energy-water Nexus, Renewable Energy, Optimization under Uncertainty, Fuzzy logic, Robust Optimization
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Energy and water systems are highly interconnected. Energy is required to extract, transmit, and treat water and wastewater, and water is needed for cooling energy systems. There is a rapid increase in demand for energy and water due to factors such as population and economic growth. In less than 30 years, the need for energy and water will nearly double globally. As the energy and water resources are limited, it is critical to have a sustainable energy-water nexus framework to meet these growing demands. Renewable energies provide substantial opportunities in energy-water nexuses by boosting energy and water reliability and sustainability and can be less water-intensive than conventional technologies. These resources, such as wind and solar power, do not need water inputs. As a result, they can be used as a supplement to the energy-water nexus portfolio. In this paper, renewable energies in energy-water nexus have been investigated for a range of possible scenarios. As renewable energy resources are not deterministic, fuzzy logic is used to model the uncertainty. The results show that renewable energies can significantly improve the energy-water nexus planning; however, the power grid reliability on renewable energy should be aligned with the level of systems uncertainty. The gap between the decisions extracted from the Fuzzy model and the deterministic model amplifies the importance of considering uncertainty to generate reliable decisions. Keywords: Energy-water Nexus, Renewable Energies, Optimization under Uncertainty, Fuzzy Logic.
In this paper we propose a novel method to establish stability and, in addition, convergence to a consensus state for a class of discrete-time Multi-Agent System (MAS) evolving according to nonlinear heterogeneous local interaction rules which is not based on Lyapunov function arguments. In particular, we focus on a class of discrete-time MASs whose global dynamics can be represented by sub-homogeneous and order-preserving nonlinear maps. This paper directly generalizes results for sub-homogeneous and order-preserving linear maps which are shown to be the counterpart to stochastic matrices thanks to nonlinear Perron-Frobenius theory. We provide sufficient conditions on the structure of local interaction rules among agents to establish convergence to a fixed point and study the consensus problem in this generalized framework as a particular case. Examples to show the effectiveness of the method are provided to corroborate the theoretical analysis.
We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator are also proven.
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then allows to rescale these sets to obtain the desired probabilistic guarantees. The proposed approach is shown to be applicable in several problem in systems and control, such as the design of Stochastic Model Predictive Control schemes or the solution of probabilistic set membership estimation problems.
The occurrence of voltage violations are a major deterrent for absorbing more roof-top solar power to smart Low Voltage Distribution Grids (LVDG). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue a novel sensitivity matrix is developed to estimate voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart photo-voltaic inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 48% compared to load flow simulations, enabling near real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The performance of the proposed methodology is analyzed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near real-time. The deviation of mean voltages of the proposed methodology from load flow method was; 6.5*10^-3 p.u for reactive power control using Q-injection, 1.02*10^-2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case.
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