Management of Demand Profiles on Mini-Grids in Developing Countries Using Timeslot Allocation

Stand-alone mini-grids provide vital energy access to rural communities across the Developing World where economic constraints necessitate optimal cost-effectiveness without compromising reliability or quality of service. Managing electricity demand to match supply availability – for example, by incentivizing consumers to operate loads at specific times – can contribute to this aim. This paper addresses a method to achieve this, whereby timeslots are sold in which additional power is made available to participating consumers with high-powered, commercial loads, such as grain mills. Using a low-cost microprocessor to control remotely-switchable power sockets by wireless communications, circuits are activated according to the timeslots purchased without interruption of low-power (e.g. lighting and phone-charging) circuits. Informed by site survey data, laboratory tests demonstrated the system to be reliable and effective in maintaining demand closer to supply availability while avoiding overloads. This reduces losses and the need for storage while increasing energy access and return on investment

Estimation of demand diversity and daily demand profile for off grid electrification in developing countries

Describes software downloadable from: https://github.com/peterboait/ESCoBox_Load_Model Open AccessThe potential for small self-contained grid systems to provide electricity for currently unserved regions of the developing world is widely recognised. However planning and managing the electrical demand that will be supported, so that a mini-grid system is not overloaded and its available resource is used as fully as possible, is actually more difficult than for a large scale grid system. This paper discusses the mathematical reasons why this is the case, and describes a practical software tool for mini-grid demand estimation and planning that is complementary to the widely used HOMER software. This software tool is made available for download on an open source basis. Finally a conclusion is offered that mini-grid systems should aim to serve at least 50 households so that demand variability is more manageable and economies of scale can be realised

Finding Robust Transition Paths for Industrial Ecosystems

In order to achieve the goal of sustainability, industries will have to make a transition to renewable energy and a more circular production. This requires substantial investments as well as constructive cooperation between the various companies in industrial clusters. The problem addressed in this thesis is to find ways of determining optimal investment paths for industrial clusters under specific constraints: the investments should contribute to sustainability (e.g., reduce emissions), provide a positive return for a cluster as a whole, and allow for a distribution of costs and benefits (e.g., through contracts) such that the companies that make the investment have strong incentives for cooperation.The idea that underlies this thesis is to represent industrial clusters as networks of processes that are owned and operated by different companies, and interdependent via their input-output flows. Such representation should then facilitate analysis from an industrial ecosystem perspective, and identification of potential investment options that would improve the cluster’s sustainability. From a game theory perspective, these investment options can then be seen as potential moves, and the companies as players. Assuming a time horizon (e.g., somewhere between 10 and 40 years), any combination of investments in that period constitutes a strategy, while for each company the difference in cumulative cash flow for that company with/without these investments constitutes the players’ payoffs. Analysis of such a multi-company investment game will reveal rational strategies. We have elaborated and tested this idea by using the Linny-R modelling language and its associated MILP optimisation tool that is being developed at TU Delft first to represent and analyse a variety of simple process configurations with only one or two investment options. Conducting this first series of small-scale simulation experiments, and verifying their outcomes has demonstrated the feasibility of our approach. Subsequently, we have applied the same approach to a realistic, albeit simplified and stylised, industrial cluster that comprises three companies. After identifying a set of potential investment options, we have used the resulting Linny R model to conduct a second series of experiments to simulate and analyse solitary investment strategies per company, a cluster-wide cooperative strategy, as well as competitive strategies with various contractual arrangements. The results of this study show that we can indeed use Linny-R as a modelling language and simulation tool to represent and analyse investment decisions as multi-actor games, which then allow us to infer and evaluate cooperative as well as competitive investment strategies. This study has several limitations. We did not investigate the scalability of the method. We experimented with a relatively small and simplified cluster; upscaling to a cluster with 10 or 100 times more processes could become computationally infeasible. Another limitation is that we did not test in practice whether models in the Linny-R notation will indeed effectively support communication and negotiation between companies. Thirdly, the set of categories of investment options that we have identified is not exhaustive. Our recommendations for future research hence are to explore the computational limits using a state-of-the-art commercial solver, and to conduct real-world case studies, meanwhile extending and refining the categories of investment options that can be instrumental in furthering the transition towards more sustainable industrial clusters.Engineering and Policy Analysi

ESCoBox: A set of tools for mini-grid sustainability in the developing world

Collaboration with Newcastle University and other NGO and commercial partners. Open Access articleMini-grids powered by photovoltaic generators or other renewable energy sources have the potential to bring electricity to the 17% of the world’s population, mainly in rural areas, that are currently un-served. However, designing and managing a mini-grid so that it is reliable and economically sustainable is difficult because of the high variability of demand that arises from the small population of consumers. We describe an integrated set of four tools to assist mini-grid operators to predict and manage demand. These comprise a decision support tool to predict peak and average demand from a consumer population, a demand disaggregation tool that allows the key statistical properties of connected electricity-consuming appliances to be identified, a battery condition modeling tool which allows the impact on battery life of a planned operating regime to be predicted and a demand control sub-system which limits the operating time of high demand appliances to intervals when they can be supported. Results from application of the tool set to mini-grids in Kenya and The Gambia are presented. We conclude that accessible, usable and low cost tools of this form can improve mini-grid sustainability

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population