Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode

To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability

Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-016-1065-xReducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.Peer ReviewedPostprint (author's final draft

Smart microgrids and virtual power plants in a hierarchical control structure

In order to achieve a coordinated integration of distributed energy resources in the electrical network, an aggregation of these resources is required. Microgrids and virtual power plants (VPPs) address this issue. Opposed to VPPs, microgrids have the functionality of islanding, for which specific control strategies have been developed. These control strategies are classified under the primary control strategies. Microgrid secondary control deals with other aspects such as resource allocation, economic optimization and voltage profile improvements. When focussing on the control-aspects of DER, VPP coordination is similar with the microgrid secondary control strategy, and thus, operates at a slower time frame as compared to the primary control and can take full advantage of the available communication provided by the overlaying smart grid. Therefore, the feasibility of the microgrid secondary control for application in VPPs is discussed in this paper. A hierarchical control structure is presented in which, firstly, smart microgrids deal with local issues in a primary and secondary control. Secondly, these microgrids are aggregated in a VPP that enables the tertiary control, forming the link with the electricity markets and dealing with issues on a larger scale

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version