Operational flexibility for increasing renewable energy penetration level by modified enhanced priority list method

The increasing concerns on climate change and the need for a more sustainable grid, recently has seen a fast expansion of renewable energy sources (RES). This leads to complexities in system balancing between the load and the integrated RES generation, as a result of increased levels of system variability and uncertainty. The concept of flexibility describes the capability of the power system to maintain a balance between generation and the load under uncertainty. Therefore, system operators need to develop flexibility measuring technique to manage the sudden intermittency of net-load. Current flexibility metrics are not exhaustive enough to capture the different aspects of the flexibility requirement assessment of the power systems. Furthermore, one of their demerits is that the start-up cost is not considered together with the other technical parameters. Hence, this thesis proposes a method that improves the assessment accuracy of individual thermal units and overall generation system. Additionally, a new flexibility metric for effective planning of system operations is proposed. The proposed metric considers technoeconomic flexibility indicators possessed by generation units. A new ranking for Flexibility Ranked Enhanced Priority List (FREPL) method for increasing share of renewable energy is proposed as well. The assessment is conducted using technical and economic flexibility indicators characteristics of the generating units. An analytical hierarchy process is utilized to assign weights to these indicators in order to measure their relative significance. Next, a normalization process is executed and then followed by a linear aggregation to produce the proposed flexibility metric. Flexibility and cost ranking are coupled in order to improve the FREPL. The proposed technique has been tested using both IEEE RTS-96 test system and IEEE 10-units generating system. The developed method is integrated with the conventional unit commitment problem in order to assist the system operators for optimal use of the generation portfolios of their power system networks. The results demonstrate that the developed metric is robust and superior to the existing metrics, while the proposed Enhanced Priority List characterizes the system’s planned resources that could be operated in a sufficiently flexible manner. The net-load profile has been enhanced and the penetration level of wind power has been upgraded from 28.9% up to 37.2% while the penetration level of solar power has been upgraded from 14.5% up to 15.1%

Harnessing flexibility potential of flexible carbon capture power plants for future low carbon power systems: Review

Fossil-fired power plants retrofitted with Carbon Capture and Storage (CCS) may have operational benefits for future low carbon power systems. This paper aims to review state of the art literature with the objective to identify whether carbon capture power plants would bring flexibility within future lower carbon power systems. To achieve this objective, at first, the work investigates flexible operation of CCS technology. In particular, flexibility enabling mechanisms and factors that affect the flexible operation of CCS are reviewed. Flexibility requirements and provision assessment tools/metrics for future low carbon power systems are reviewed with the aim to identify the favourite properties required for future low carbon technologies. The work then presents how flexible CCS might improve the conventional power plant flexibility properties. Moreover, the paper presents the value of flexible operation of CCS for different stakeholders while it also identifies different influencing factors of optimal plant operation and profitability. The different power system services that the CCS-equipped plants might serve are also presented

Optimal strategies modeling in electricity market for electric vehicles integration in presence of intermittent resources

Electric vehicles (EVs) as an alternative to the current fossil fuel vehicles represent the most promising green approach to electrification of an important portion of the global transportation sector. This uncertain load brings new challenges to market-oriented demand response programs (DRPs) specifically in the presence of renewable energy resources (RER). Being a special type of load, EVs are highly capable of providing a significant amount of flexible load demand through participating in various types of DRPs, while using their battery storage potentials allows a higher penetration level of intermittent RER in the grid. Therefore, there is a strong need to increase EV owner’s participation in the market by providing attractive financial benefit-based decision-making tools and simplifying the market process to enhance system reliability and reduce price volatility. In this paper, a novel optimal decision-making methodology is proposed which, unlike previous works, utilizes a grid characteristic’s model within a game-theoretical approach, conflicting and capturing economic interests of both players together and evaluates the optimum strategies for a successful market operation in simplest way. This approach can facilitate both EV owners and utilities to derive their robust bidding strategies, in which they can create a simple business case analysis to weigh their benefits of participation in the market. To evaluate the performance, a simulation framework with uncertain load demands and generation has been developed and compared. The results show that the proposed strategy is appropriate for use in real-time automated DRPs

A novel constraint handling approach for metaheuristic techniques in solving economic dispatch problems

This paper proposes a novel constraint handling strategy (CHM) based on random walk for metaheuristic techniques in solving the optimal economic dispatch (ED) problems. To implement this CHM, a Cuckoo Search (CS) algorithm has been adopted. The absolute as well as the relative performance of the resultant hybrid algorithm is experimentally investigated using a standard test case with valve point effects. Statistical parameters are used in order to evaluate the robustness of the method. The proposed methodology proves that it outperforms established methods such as particle swarm optimization and genetic algorithm methods in terms of robustness and achieving consistent results throughout all the trials in each experiment

Sustainable Services to Enhance Flexibility in the Upcoming Smart Grids

Global efforts are already focusing on future targets for even more increases in renewable energy sources contribution, greater efficiency improvements and further greenhouse gas emission reductions. With the fast-paced changing technologies in the context of sustainable development, new approaches and concepts are needed to cope with the variability and uncertainty affecting generation, transmission and load demand. The main challenge remains in developing technologies that can efficiently make use of the available renewable resources. Alternatives in the form of microgrids or virtual power plants along with electricity storage are potential candidates for enhancing flexibility. However, intelligence must be added at all levels in the grid and among all the equipment comprising each subsystem, in order to achieve two-way communications and bidirectional flow of power. Then, the concept of smart grid can be realized and, relying upon software systems, it can remotely and automatically dispatch and optimize generation or storage resources in a single, secure and Web-connected way. Deploying smart configurations and metering promises new possibilities for self-managed energy consumption, improved energy efficiency among final consumers and transition to more consumer-centric energy systems via demand response and demand-side management mechanisms