Online Modified Greedy Algorithm for Storage Control under Uncertainty

This paper studies the general problem of operating energy storage under uncertainty. Two fundamental sources of uncertainty are considered, namely the uncertainty in the unexpected fluctuation of the net demand process and the uncertainty in the locational marginal prices. We propose a very simple algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A stylized analysis for the algorithm is performed, which shows that comparing to the optimal cost of the corresponding stochastic control problem, the sub-optimality of OMG is bounded and approaches zero in various scenarios. This suggests that, albeit simple, OMG is guaranteed to have good performance in some cases; and in other cases, OMG together with the sub-optimality bound can be used to provide a lower bound for the optimal cost. Such a lower bound can be valuable in evaluating other heuristic algorithms. For the latter cases, a semidefinite program is derived to minimize the sub-optimality bound of OMG. Numerical experiments are conducted to verify our theoretical analysis and to demonstrate the use of the algorithm.Comment: 14 page version of a paper submitted to IEEE trans on Power System

Intelligent control of PV co-located storage for feeder capacity optimization

Battery energy storage is identified as a strong enabler and a core element of the next generation grid. However, at present the widespread deployment of storage is constrained by the concerns that surround the techno-economic viability. This thesis addresses this issue through optimal integration of storage to improve the efficiency of the electricity grid. A holistic approach to optimal integration includes the development of methodologies for optimal siting, sizing and dispatch coordination of storage

Improving data center efficiency through smart grid integration and intelligent analytics

The ever-increasing growth of the demand in IT computing, storage and large-scale cloud services leads to the proliferation of data centers that consist of (tens of) thousands of servers. As a result, data centers are now among the largest electricity consumers worldwide. Data center energy and resource efficiency has started to receive significant attention due to its economical, environmental, and performance impacts. In tandem, facing increasing challenges in stabilizing the power grids due to growing needs of intermittent renewable energy integration, power market operators have started to offer a number of demand response (DR) opportunities for energy consumers (such as data centers) to receive credits by modulating their power consumption dynamically following specific requirements. This dissertation claims that data centers have strong capabilities to emerge as major enablers of substantial electricity integration from renewables. The participation of data centers into emerging DR, such as regulation service reserves (RSRs), enables the growth of the data center in a sustainable, environmentally neutral, or even beneficial way, while also significantly reducing data center electricity costs. In this dissertation, we first model data center participation in DR, and then propose runtime policies to dynamically modulate data center power in response to independent system operator (ISO) requests, leveraging advanced server power and workload management techniques. We also propose energy and reserve bidding strategies to minimize the data center energy cost. Our results demonstrate that a typical data center can achieve up to 44% monetary savings in its electricity cost with RSR provision, dramatically surpassing savings achieved by traditional energy management strategies. In addition, we investigate the capabilities and benefits of various types of energy storage devices (ESDs) in DR. Finally, we demonstrate RSR provision in practice on a real server. In addition to its contributions on improving data center energy efficiency, this dissertation also proposes a novel method to address data center management efficiency. We propose an intelligent system analytics approach, "discovery by example", which leverages fingerprinting and machine learning methods to automatically discover software and system changes. Our approach eases runtime data center introspection and reduces the cost of system management.2018-11-04T00:00:00

An interdisciplinary review of energy storage for communities: challenges and perspectives

Given the increasing penetration of renewable energy technologies as distributed generation embedded in the consumption centres, there is growing interest in energy storage systems located very close to consumers. These systems allow to increase the amount of renewable energy generation consumed locally, they provide opportunities for demand-side management and help to decarbonise the electricity, heating and transport sectors. In this paper, the authors present an interdisciplinary review of community energy storage (CES) with a focus on its potential role and challenges as a key element within the wider energy system. The discussion includes: the whole spectrum of applications and technologies with a strong emphasis on end user applications; techno-economic, environmental and social assessments of CES; and an outlook on CES from the customer, utility company and policy-maker perspectives. Currently, in general only traditional thermal storage with water tanks is economically viable. However, CES is expected to offer new opportunities for the energy transition since the community scale introduces several advantages for electrochemical technologies such as batteries. Technical and economic benefits over energy storage in single dwellings are driven by enhanced performance due to less spiky community demand profile and economies of scale respectively. In addition, CES brings new opportunities for citizen participation within communities and helps to increase awareness of energy consumption and environmental impacts

Challenges and prospects of renewable hydrogen-based strategies for full decarbonization of stationary power applications

The exponentially growing contribution of renewable energy sources in the electricity mix requires large systems for energy storage to tackle resources intermittency. In this context, the technologies for hydrogen production offer a clean and versatile alternative to boost renewables penetration and energy security. Hydrogen production as a strategy for the decarbonization of the energy sources mix has been investigated since the beginning of the 1990s. The stationary sector, i.e. all parts of the economy excluding the transportation sector, accounts for almost three-quarters of greenhouse gases (GHG) emissions (mass of CO2-eq) in the world associated with power generation. While several publications focus on the hybridization of renewables with traditional energy storage systems or in different pathways of hydrogen use (mainly power-to-gas), this study provides an insightful analysis of the state of art and evolution of renewable hydrogen-based systems (RHS) to power the stationary sector. The analysis started with a thorough review of RHS deployments for power-to-power stationary applications, such as in power generation, industry, residence, commercial building, and critical infrastructure. Then, a detailed evaluation of relevant techno-economic parameters such as levelized cost of energy (LCOE), hydrogen roundtrip efficiency (HRE), loss of power supply probability (LPSP), self-sufficiency ratio (SSR), or renewable fraction (fRES) is provided. Subsequently, lab-scale plants and pilot projects together with current market trends and commercial uptake of RHS and fuel cell systems are examined. Finally, the future techno-economic barriers and challenges for short and medium-term deployment of RHS are identified and discussed.This research is being supported by the Project ENERGY PUSH SOE3/P3/E0865, which is co-financed by the European Regional Development Fund (ERPF) in the framework of the INTERREG SUDOE Programme and the Spanish Ministry of Science, Innovation, and Universities (Project: RTI2018-093310-B-I00)