Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

Day-Ahead Scheduling for Economic Dispatch of Combined Heat and Power with Uncertain Demand Response

This paper presents an energy management method for the interconnected operation of power, heat, Combined Heat and Power (CHP) units to settle the Day-Ahead market in the presence of a demand response program (DRP). A major challenge in this regard is the price uncertainty for DRP participants. First, the definitive model of the problem is introduced from the perspective of the Regional Market Manager (RMM) in order to minimize the total supply cost in the presence of TOU program, which is a type of DRP. Furthermore, a market-oriented tensile model is presented in the form of a combination of over-lapping generations (OLG) and price elasticity (PE) formulations to determine the amount of electricity demand in the TOU program. Then, a price uncertainty model of the proposed problem is introduced according to the IGDT risk aversion and risk-taking strategies considering information gap decision theory (IGDT). The above problem is solved through the use of the co-evolutionary particle swarm optimization (C-PSO) algorithm and the proposed model is implemented on a standard seven-unit system for a period of 24 hours.© 2022 authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

CENTRALIZED AND DISTRIBUTED OPTIMIZATION OF LOAD DISPATCH PROBLEM INCORPORATING DISTRIBUTED GENERATION

Ph.DDOCTOR OF PHILOSOPH

Planning integrated energy systems coupling V2G as a flexible storage

Coupling the vehicle-to-grid (V2G) with integrated energy systems (IES) offers an emerging solution for decarbonisation of both energy and transport sectors. To evaluate the feasibility of coupling V2G with IES as a flexible storage, we propose an optimisation-based system planning framework embedding V2G into IES. Within this framework, stochastic features of electric vehicles (EV) fleets are simulated. The impacts of V2G on IES design are captured by assessing both economic and environmental benefits via multi-objective optimisations utilising an improved NSGA-II algorithm. Six case studies considering three cities with different climate conditions and two functional areas of residential and commercial are performed. The results manifest that Beijing-commercial case could achieve the largest mutual benefits. The EV fleets’ charging behaviour follows the time-of-use energy tariff in transition seasons while not during winter. Sensitivity analysis indicates the electricity and gas prices have significant impact on the system design. The benefits induced by growing EV penetration would gradually decrease and stabilise when the EV number reach 300, the growth of economic and environmental benefits stabilized at 1.3% and 1.8%, respectively. Overall, this study quantifies the benefits of enabling V2G in IES, and generates valuable insights for IES planners, V2G service providers, and relevant policymakers

Multi-objective optimization of CCHP system with hybrid chiller under new electric load following operation strategy

The performance of combined cooling, heating and power (CCHP) system is greatly affected by its operating strategy and design. In this paper, a new electric load following (NELF) strategy was developed. It is based on the alternation between absorption cooling and electric cooling according to the building energy requirements, for hybrid chiller based CCHP systems. A comparison of the new proposed strategy with the modified electric load following (MELF) and electric load following (ELF) strategies is performed. A multi-objective optimization approach based on genetic algorithm is carried out to predict the optimal capacity of CCHP systems. Performance criteria like primary energy consumption, annual total cost and carbon dioxide emission were considered as objective functions. The performances of these CCHP systems and operation strategies were examined and compared with the separated production (SP) system for a Mosque complex located in Algiers, Algeria. Results show that hybrid chiller CCHP based NELF strategy is the best choice, which can reduce the primary energy consumption by 34.45 GWh/year, annual total cost by 0.313 million €/year and carbon dioxide emission by 8.37 kton/year. Compared to the other configurations and strategies, the hybrid CCHP based NELF achieves better energetic, economic and environmental performance under the optimized conditions