More Like this

1. Blue food demand across geographic and temporal scales

   https://doi.org/10.1038/s41467-021-25516-4  

   Naylor, Rosamond L.; Kishore, Avinash; Sumaila, U. Rashid; Issifu, Ibrahim; Hunter, Blaire P.; Belton, Ben; Bush, Simon R.; Cao, Ling; Gelcich, Stefan; Gephart, Jessica A.; et al (December 2021, Nature Communications)

   Abstract Numerous studies have focused on the need to expand production of ‘blue foods’, defined as aquatic foods captured or cultivated in marine and freshwater systems, to meet rising population- and income-driven demand. Here we analyze the roles of economic, demographic, and geographic factors and preferences in shaping blue food demand, using secondary data from FAO and The World Bank, parameters from published models, and case studies at national to sub-national scales. Our results show a weak cross-sectional relationship between per capita income and consumption globally when using an aggregate fish metric. Disaggregation by fish species group reveals distinct geographic patterns; for example, high consumption of freshwater fish in China and pelagic fish in Ghana and Peru where these fish are widely available, affordable, and traditionally eaten. We project a near doubling of global fish demand by mid-century assuming continued growth in aquaculture production and constant real prices for fish. Our study concludes that nutritional and environmental consequences of rising demand will depend on substitution among fish groups and other animal source foods in national diets. 

   more » « less
2. Energy Management for Prepaid Customers: A Linear Optimization Approach

   https://doi.org/10.1109/SmartGridComm60555.2024.10738077  

   Marathe, Maitreyee; Roald, Line A (September 2024, IEEE)

   With increasing energy prices, low income households are known to forego or minimize the use of electricity to save on energy costs. If a household is on a prepaid electricity program, it can be automatically and immediately disconnected from service if there is no balance in its prepaid account. Such households need to actively ration the amount of energy they use by deciding which appliances to use and for how long. We present a tool that helps households extend the availability of their critical appliances by limiting the use of discretionary ones, and prevent disconnections. The proposed method is based on a linear optimization problem that only uses average power demand as an input and can be solved to optimality using a simple greedy approach. We compare the model with two mixed-integer linear programming models that require more detailed demand forecasts and optimization solvers for implementation. In a numerical case study based on real household data, we assess the performance of the different models under different accuracy and granularity of demand forecasts. Our results show that our proposed linear model is much simpler to implement, while providing similar performance under realistic circumstances 

   more » « less
3. Data-driven pricing of demand response

   https://doi.org/10.1109/SmartGridComm.2016.7778765  

   Khezeli, Kia; Bitar, Eilyan (November 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm))

   We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over T days that is at most O(√T), relative to an oracle who knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense. 

   more » « less
4. Risk-Sensitive Learning and Pricing for Demand Response

   https://doi.org/10.1109/TSG.2017.2700458  

   Khezeli, Kia; Bitar, Eilyan (May 2017, IEEE Transactions on Smart Grid)

   We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over T days that is at most O( p T), relative to an oracle pricing policy that knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense. 

   more » « less
5. Opportunities for Productive Uses of Electricity in the Off-Grid Market

   https://doi.org/10.1109/PowerAfrica61624.2024.10759476  

   Muza, Olivia; Thomas, Valerie M (October 2024, IEEE)

   Increased demand for productive uses of energy is a driver of energy consumption. Low-income households in the off-grid market have a budget constraint. By understanding the energy priorities of low-income households in the off-grid market, utilities can develop innovative solutions for servicing the market. This paper builds on recent literature on productive uses of electricity for increased investment and pay back for micro-grid investment across the eight productive user categories that were developed in Rwanda. We ask: how can the range, success, and benefits of productive uses of energy be expanded in resource-constrained settings? To answer the research question, we address the need for supporting infrastructure of business development services, energy services bundling, and utility policy instruments that support productive use of energy. We created metrics for prioritizing productive uses of energy in the off-grid market. In the face of a budget constraint, low-income households that prioritize domestic over business and services uses of energy need integrated services that support the consumptive-productive-service link. Realistic cost-benefit analyses are needed. Solar irrigation as a small industry case in Rwanda can achieve at best a 3-year payback period if it can increase productivity by 54%. We offer recommendations to increase energy demand from productive uses of energy in the off-grid market: work with local cultural norms to support business development; develop realistic cost-benefit analyses based on local data, and partner with business training service providers. 

   more » « less