Photovoltaics as a terrestrial energy source. Volume 2: System value

Assumptions and techniques employed by the electric utility industry and other electricity planners to make estimates of the future value of photovoltaic (PV) systems interconnected with U.S. electric utilities were examined. Existing estimates of PV value and their interpretation and limitations are discussed. PV value is defined as the marginal private savings accruing to potential PV owners. For utility-owned PV systems, these values are shown to be the after-tax savings in conventional fuel and capacity displaced by the PV output. For non-utility-owned (distributed) systems, the utility's savings in fuel and capacity must first be translated through the electric rate structure (prices) to the potential PV system owner. Base-case estimates of the average value of PV systems to U.S. utilities are presented. The relationship of these results to the PV Program price goals and current energy policy is discussed; the usefulness of PV output quantity goals is also reviewed

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Active Management of Distributed Generation based on Component Thermal Properties

Power flows within distribution networks are expected to become increasingly congested with the proliferation of distributed generation (DG) from renewable energy resources. Consequently, the size, energy penetration and ultimately the revenue stream of DG schemes may be limited in the future. This research seeks to facilitate increased renewable energy penetrations by utilising power system component thermal properties together with DG power output control techniques. The real-time thermal rating of existing power system components has the potential to unlock latent power transfer capacities. When integrated with a DG power output control system, greater installed capacities of DG may be accommodated within the distribution network. Moreover, the secure operation of the network is maintained through the constraint of DG power outputs to manage network power flows. The research presented in this thesis forms part of a UK government funded project which aims to develop and deploy an on-line power output control system for wind-based DG schemes. This is based on the concept that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on component cooling mechanisms. The control system compares component real-time thermal ratings with network power flows and produces set points that are fed back to the DG for implementation. The control algorithm comprises: (i) An inference engine (using rule-based artificial intelligence) that decides when DG control actions are required; (ii) a DG set point calculator (utilising predetermined power flow sensitivity factors) that computes updated DG power outputs to manage distribution network power flows; and (iii) an on-line simulation tool that validates the control actions before dispatch. A section of the UK power system has been selected by ScottishPower EnergyNetworks to form the basis of field trials. Electrical and thermal datasets from the field are used in open loop to validate the algorithms developed. The loop is then closed through simulation to automate DG output control for increased renewable energy penetrations

Behavioral types in programming languages

A recent trend in programming language research is to use behav- ioral type theory to ensure various correctness properties of large- scale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their represen- tation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to de- sign and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types

ESSAYS ON BUSINESS ENVIRONMENT AND FIRM PERFORMANCE

Firm performance is central to economic growth of developing economies. However, it is affected by the business environments in which a firm operates. These business environments includes: features of legal and regulatory services, infrastructures, financial and institutional systems of the country. A burgeoning literature within development economics seeks to understand the constraints that a firm face and strategies to cope with these problems. However, a rigorous empirical study that informs policy makers and concerned development institutions is still lacking especially in Sub-Sahara African countries where the problem is severe. Thus, this thesis focused on examining the impact of business environment on firm performance and how firms respond to poor business environment. The study mainly focused on examining the impact of poor electricity supply, its economic cost and how firms responds to a poor power supply. The thesis is organized in two chapters. The first chapter \u201cpower outages, economic cost and firm performance: Evidence from Ethiopia\u201ddeals with how firms in Ethiopia respond to power interruptions and estimating the economic cost of power outages using two rounds of firm-level survey data. The study employed the World Bank Enterprise Survey (WBES) data collected from firms operating in Ethiopia during 2011 and 2015. The result shows that firms in Ethiopia self-generate electricity in response to power outages. Power outages were found to affect firms\u2019 productivity negatively, increasing firms\u2019 costs by 15% from 2011 to 2015. This effect varied negatively with output level, suggesting that power outages is particularly costly for small firms. This chapter is a single authored paper and published in the Journal of Utilities Policy (53) 111-120. The article can be accessed from: https://doi.org/10.1016/j.jup.2018.06.009. The second chapter \u201cfirm performance under infrastructure constraint: evidence from Sub-Saharan African firms\u201d deals with the role of investment in self-generation in mitigating outage loss and evaluating the outage loss differential between firms that invested in self-generation and those that didn't. Using the WBES data collected from firms operating in 13 Sub-Saharan African countries, the study provided an evidence that though self-generation has helped firms reduce outage loss, firms that have invested in self-generation continue to face higher unmitigated outage loss compared to firms without such investment. In spite of this, firms that have invested in self-generation would have incurred 36%-99% more than their current outage loss if they didn't engage in self-generation while firms that didn't invest in self-generation would have reduced their outage loss by 2% - 24% if they had engaged in self generation. This chapter is also a single-authored paper. Given the above result, the study proposed a differential supply interruption to be followed by public authorities based on firms' degree of vulnerability. Stating differently, firms whose operation are more vulnerable to power outages should get preferential power supply advantage. This could be possible by arranging a binding contract between a vulnerable firms and power companies, so that power companies charge an optimal tariff for supplying secure power for vulnerable firms. In turn, firms should be compensated if the power companies fail to do so. This helps vulnerable firms expand their production without fearing the risk of power outage

Transmission congestion management by optimal placement of FACTS devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 13/09/2010.This thesis describes the implementation of the Flexible AC Transmission Systems (FACTS) devices to develop a market-based approach to the problem of transmission congestion management in a Balancing Market. The causes, remedies and pricing methods of transmission congestion are briefly reviewed. Balancing Market exists in markets in which most of the trading is done via decentralized bilateral contracts. In these markets only final adjustments necessary to ensure secure system operation is carried out at a centralized Balancing Market. Each market player can participate in the Balancing Market by submitting offers and bids to increase and decrease its initially submitted active generation output. In this research a method is proposed to reduce costs associated with congestion re-dispatch in a Balancing Market by optimal placement of FACTS devices, and in particular Thyristor Controlled Phase Shifter Transformers (TCPST). The proposed technique is applicable to both Mixed Integer Linear Programming (MILP) and Mixed Integer Non-Linear Programming (MINLP). In the MILP a power system network is represented by a simplified DC power flow under a MILP structure and the Market participants' offers and bids are also represented by linear models. Results show that applications of FACTS devices can significantly reduce costs of congestion re-dispatch. The application of the method based on the MINLP creates a nonlinear and non-convex AC OPF problem that might be trapped in local sub-optima solutions. The reliability of the solution that determines the optimal placement of FACTS devices is an important issue and is carried out by investigation of alternative solvers. The behavior of the MINLP solvers is presented and finally the best solvers for this particular optimization problem are introduced. The application of DC OPF is very common in industry. The accuracy of the DC OPF results is investigated and a comparison between the DC and AC OPF is presented

Determinants and Impacts of Demand-side Management Program Investment of Electric Utilities

From the late seventies through the early 1990\u27s electric utilities were facing many different forces that caused them to invest into demand-side management programs (DSM). Roots of the growth of DSM can be found in the high inflation and energy price shocks of the late seventies and early eighties, spiraling building costs of generation, safety and environmental concerns, increased costs of new capacity with possible exhaustion of scale economies, unexpected high elasticity in the demand for electricity, and public utility commissions that sought alternatives to the resulting high rate increases. This study develops and estimates four equations that look at the more aggregate utility level impacts of DSM. The goal of two equations is to determine what factors influence utility investments in DSM and if stock market investment in utilities is affected by DSM. Two additional equations are developed to determine system level impacts of DSM on cost of and quantity demanded of electricity. To estimate these models four years of annual data were collected for 81 utilities spanning 1990-1993. These utilities have sold over 60% of all the electricity in the US and were responsible for over 80% the national spending in DSM. The DSM investment model indicated that of the major variance in DSM investment is due to the utility\u27s regulatory environment. Both an above average regulatory climate and least-cost planning requirements had major impacts on the level of DSM investment. The cost of equity capital equation revealed that DSM expenditures had a positive impact on the valuation of utility\u27s stock. Cost and quantity equations were estimated both individually and simultaneously. DSM expenditures seemed to have a negative impact on both average cost and quantity demanded. Although these relationships were statistically significant, the impacts were quite small. To summarize; the regulatory environment seems to have the strongest impact on the level of DSM investment; DSM spending was associated with an increased stock valuation; as expected DSM investments were found to have a negative relationship with quantity demanded; and finally DSM investment appeared to reduce the average cost

Interactive Demand Shifting in the context of Domestic Micro-Generation

The combination of ubiquitous computing and emerging energy technologies is radically changing the home energy landscape. Domestic micro-generation, dominated by solar photovoltaic, is increasing at a rapid pace. This represents an opportunity for creating and altering energy behaviours. However, these transformations generate new challenges that we call the domestic energy gap: domestic electricity consumption and microgeneration are out of sync. Micro-generation is mainly uncontrollable production relying on weather while domestic energy consumption tends to happen mostly during the evening. This thesis focuses on understanding and supporting new domestic practices in the context of domestic solar electricity generation, looking at ‘Demand-Shifting’. Specifically, we look at how can digital tools leverage Demand-Shifting practices in the context of domestic micro-generation? Relying on a mixed-method approach, we provide a qualitative and quantitative answer with the collaboration of 38 participating households in several field studies including two spanning more than eight months. Through a deep investigation of laundry and electric mobility routines in the context of domestic micro-generation, we emphasised a natural engagement into Demand-Shifting which appeared as a complex and time-consuming task for participants which was not visible when we analysed their quantitative data. We revealed this complexity through Participatory Data Analyses, a method we designed to analyse the data in collaboration with the participating householders. This provided us with a comprehensive view of the relationship between domestic micro-generation and daily routines. Finally, we highlight the need for timely and contextual support through the deployment of interventions in-the-wild. Building on discussions of our findings in perspective of the literature, we propose a conceptual framework to support domestic interactive Demand-Shifting

Dynamic Demand Response in Residential Prosumer Collectives

This research aims at exploring how smart grid opportunities can be leveraged to ameliorate demand response practices for residential prosumer collectives, while meeting the needs of end-users and power grids. Electricity has traditionally been generated in centralized plants then transmitted and distributed to end-users, but the increasing cost-effectiveness of micro-generation (e.g. solar photovoltaics) is resulting in the growth of more decentralized generation. The term "prosumers" is commonly used to refer to energy users (usually households) who engage in small-scale energy production. Of particular interest is the relatively new phenomenon of prosumer collectives, which typically involve interactions between small-scale decentralized generators to optimize their collective energy production and use through sharing, storing and/or trading energy. Drivers of collective prosumerism include sustaining community identity, optimizing energy demand and supply across multiple households, and gaining market power from collective action. Managing power flows in grids integrating intermittent micro-generation (e.g. from solar photovoltaics and micro-wind turbines) presents a challenge for prosumer collectives as well as power grid operators. Smart grid technologies and capabilities provide opportunities for dynamic demand response, where flexible demand can be better matched with variable supply. Ideally, smart grid opportunities should incentivize prosumers to maximize their energy self-consumption from local supply while fairly sharing any income from trading surplus energy, or any loss of utility associated with altering energy demand patterns. New businesses are emerging and developing various products and services around smart grid opportunities to cater for the socio-technical needs of residential prosumer collectives, where technical energy systems overlap with social interactions. This research studies how emerging businesses are using smart grid capabilities to create dynamic demand response solutions for residential prosumer collectives, and how fairness can be adopted in solutions targeting those collectives. This research interweaves social and technical knowledge from literature to interpret the interactions and objectives of prosumer collectives in new ways, and create new socio-technical knowledge around those interpretations. Conducting this research involved using mixed research methods to draw on social science, computer science, and power systems. In the social stream of the research, semi-structured interviews were conducted with executives in businesses providing current or potential smart grid solutions enabling dynamic demand response in residential prosumer collectives. In the technical stream, optimization, computation and game theory concepts were used to develop software algorithms for integrating fairness in allocating shared benefits and loss of utility in collective settings. Interview findings show that new business models and prosumer-oriented solutions are being developed to support the growth of prosumer collectives. Solutions are becoming more software-based, and enabling more socially-conscious user choice. Challenges include dealing with power quality rather than capacity, developing scalable business models and adequate regulatory frameworks, and managing social risks. Automated flexibility management is anticipated to dominate dynamic demand response practices, while the grid is forecast to become one big prosumer community rather than pockets of closed communities. Additionally, the research has developed two software algorithms for residential collectives, to fairly distribute revenue and loss of utility among households. The algorithms used game theory, optimization and approximation algorithms to estimate fair shares with high accuracy using less computation time and memory resources than exact methods