Search for: All records

Smart grids use digital information technology to simultaneously increase energy efficiency while integrating renewables into the electric grid, making it a critical component of achieving a low-carbon energy system. Prior research on the social acceptance of smart grids has relied on either single time point assessment (i.e. prior to a smart grid rollout) or experimental and lab settings. These approaches miss key aspects of social acceptance because they fail to capture change over time through the interaction between stakeholders, technology, and utilities. In contrast, we compare two waves of survey data on the social acceptance of smart grid technologies, the first (n= 609) prior to a local rollout of a smart grid program in upstate New York and the second (n= 533) two years after the same rollout. Our results demonstrate that in contrast to the hopes of smart energy advocates, the social acceptance of four dimensions of smart grids either remain steady or decline over time. Further analyses reveal that the factors that shape acceptance also change over time. This study demonstrates that the social acceptance of smart grids may actually decrease over time even with the robust engagement of consumers, not only challenging optimistic views of smart grid technology but also challenging broader theoretical arguments in the literature on the social acceptance of energy technologies.

 

We investigate a data-driven approach to constructing uncertainty sets for robust optimization problems, where the uncertain problem parameters are modeled as random variables whose joint probability distribution is not known. Relying only on independent samples drawn from this distribution, we provide a nonparametric method to estimate uncertainty sets whose probability mass is guaranteed to approximate a given target mass within a given tolerance with high confidence. The nonparametric estimators that we consider are also shown to obey distribution-free finite-sample performance bounds that imply their convergence in probability to the given target mass. In addition to being efficient to compute, the proposed estimators result in uncertainty sets that yield computationally tractable robust optimization problems for a large family of constraint functions. 

We consider the decentralized control of a discretetime, linear system subject to exogenous disturbances and polyhedral constraints on the state and input trajectories. The underlying system is composed of a finite collection of dynamically coupled subsystems, where each subsystem is assumed to have a dedicated local controller. The decentralization of information is expressed according to sparsity constraints on the state measurements that each local controller has access to. In this context, we investigate the design of decentralized controllers that are affinely parameterized in their measurement history. For problems with partially nested information structures, the optimization over such policy spaces is known to be convex. Convexity is not, however, guaranteed under more general (nonclassical) information structures in which the information available to one local controller can be affected by control actions that it cannot access or reconstruct. With the aim of alleviating the nonconvexity that arises in such problems, we propose an approach to decentralized control design where the information-coupling states are effectively treated as disturbances whose trajectories are constrained to take values in ellipsoidal contract sets whose location, scale, and orientation are jointly optimized with the underlying affine decentralized control policy. We establish a natural structural condition on the space of allowable contracts that facilitates the joint optimization over the control policy and the contract set via semidefinite programming. 

Electrification of vehicles is becoming one of the main avenues for decarbonization of the transportation market. To reduce stress on the energy grid, large-scale charging will require optimal scheduling of when electricity is delivered to vehicles. Coordinated electric-vehicle charging can produce optimal, flattened loads that would improve reliability of the power system as well as reduce system costs and emissions. However, a challenge for successful introduction of coordinated deadline-scheduling of residential charging comes from the demand side: customers would need to be willing both to defer charging their vehicles and to accept less than a 100% target for battery charge. Within a coordinated electric-vehicle charging pilot run by the local utility in upstate New York, this study analyzes the necessary incentives for customers to accept giving up control of when charging of their vehicles takes place. Using data from a choice experiment implemented in an online survey of electric-vehicle owners and lessees in upstate New York (N=462), we make inference on the willingness to pay for features of hypothetical coordinated electric-vehicle charging programs. To address unobserved preference heterogeneity, we apply Variational Bayes (VB) inference to a mixed logit model. Stochastic variational inference has recently emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of discrete choice models. Our results show that individuals negatively perceive the duration of the timeframe in which the energy provider would be allowed to defer charging, even though both the desired target for battery charge and deadline would be respected. This negative monetary valuation is evidenced by an expected average reduction in the annual fee of joining the charging program of $2.64 per hour of control yielded to the energy provider. Our results also provide evidence of substantial heterogeneity in preferences. For example, the 25% quantile of the posterior distribution of the mean of the willingness to accept an additional hour of control yielded to the utility is $5.16. However, the negative valuation of the timeframe for deferring charging is compensated by positive valuation of emission savings coming from switching charging to periods of the day with a higher proportion of generation from renewable sources. Customers also positively valued discounts in the price of energy delivery. 

Energy markets are rapidly changing with smarter, connected, more reliable infrastructure and cleaner generation on the supply side, and more choice, greater control and enhanced flexibility for customers. This paper examines willingness to pay for bundled smart home energy products and information services, using data from a set of two discrete choice experiments that were part of a survey by the regional energy provider of upstate New York. To let the data reveal how preferences are distributed in the population, a logit-mixed logit model in willingness-to-pay space and a combination of observed and unobserved preference heterogeneity was specified and fitted. Results show that residents of Tompkins County are willing to pay more than in other counties for residential storage, and that for home energy management there is an important generational divide with millennials being much more likely to perceive the economic value in the smart energy technologies. The flexible logit-mixed logit estimates provide evidence of important heterogeneity in preferences: whereas most of the population has a positive –albeit rather low– valuation of smart energy products and services, there is a considerable percentage of customers with negative perceptions.