State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch

This paper concerns state estimation problems in a mean field control setting. In a finite population model, the goal is to estimate the joint distribution of the population state and the state of a typical individual. The observation equations are a noisy measurement of the population. The general results are applied to demand dispatch for regulation of the power grid, based on randomized local control algorithms. In prior work by the authors it has been shown that local control can be carefully designed so that the aggregate of loads behaves as a controllable resource with accuracy matching or exceeding traditional sources of frequency regulation. The operational cost is nearly zero in many cases. The information exchange between grid and load is minimal, but it is assumed in the overall control architecture that the aggregate power consumption of loads is available to the grid operator. It is shown that the Kalman filter can be constructed to reduce these communication requirements,Comment: To appear, IEEE Trans. Auto. Control. Preliminary version appeared in the 54rd IEEE Conference on Decision and Control, 201

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

Lowering the pirate flag: a TPB study of the factors influencing the intention to pay for movie streaming services

The launch of several movie streaming services has raised new questions about how online consumers deal with both legal and illegal options to obtain their desired products. This paper investigates the factors influencing consumers’ intentions to subscribe to online movie streaming services. These services have challenged the dramatic growth in their illegal counterpart in recent years. Taking the theory of planned behavior as a starting point, we extended existing models in the literature by incorporating factors that are specific to consumer behavior in this particular field. A quantitative survey was conducted for the Italian market, and structural equation modeling was used for data analysis. Attitudes, involvement with products, moral judgement and frequency of past behavior were found to be the most important factors in explaining the intention to pay for movie streaming services. The paper provides insights for policy makers and industry managers on the marketing communication strategies needed to minimize the risk of digital piracy

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Integration of renewable energy into Nigerian power systems

Many countries are advancing down the road of electricity privatization, deregulation, and competition as a solution to their growing electricity demand and other challenges posed by the monopolistic nature of the existing structure. Presently, Nigeria has a supply deficit of electricity as a result of the growing demand. This imbalance has negatively affected the economy of the country and the social-economic well-being of the population. Hence, there is an urgent need to reform the power sector for greater efficiency and better performance. The objectives of the reform are to meet the growing power demand by increasing the electric power generation and also by increasing competitiveness through the participation of more private sector entities. The renewable energy integration is one way of increasing the electricity generation in the country in order to cater for the growing demand adequately. Examples of the renewable energy that is available in the country include wind, geothermal, solar and hydro. They are considered to be environmentally friendly, replenishable and do not contribute to the climate change phenomena. The country presently generates the bulk of its electricity from both thermal (85%) and hydroelectric (15%) power plants. While electricity generation from the thermal power stations constitutes the largest share of greenhouse emission, this is mostly from burning coal and natural gas. The effect of this high proportion of greenhouse emission causes climate change which is referred to as a variation in the climate system statistical properties over a long period of time. It has been observed that many of the activities of human beings are contributory factors to the release of these greenhouse gases (GHG). But, as the traditional sources of energy continue to threaten the present and future existence on the planet earth, it is, therefore, imperative to increase the integration of the variable renewable energy sources in a sustainable and eco-friendly manner over a long period of time. The variability and the uncertainties of the renewable energy source's output, present a major challenge in the design of an efficient electricity market in a deregulated environment. The system deregulation and the use of renewable sources for the generation of electricity are major changes presently being experienced in power system. In a deregulated power system, the integration of renewable generation and its penetration affects both the physical and the economic operations. The main focus of this research is on the integration of wind energy into Nigerian power systems. Up till now, research on the availability of the wind energy and its economic impacts has been limited in Nigeria. Generally, the previous study of wind energy availability in Nigeria has been limited in scope. The wind energy assessment study has not been detailed enough to be able to ascertain the wind energy potential of the country. To cope with this shortcoming, a detailed statistical wind modeling and forecasting methodology have been used in this thesis to determine the amount of extractable wind energy in six selected locations in Nigeria using historical wind speed data for 30 years. The accuracy test of the statistical models was also carried using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Chi-Square methods to determine the inherent error margin in the modeling and analysis. It is found that the error margin of the evaluations falls within the expected permissible tolerance range. For a more detailed wind assessment study of the Nigeria weather, the seasonal variation of the weather conditions as it affects the wind speed and availability during the two major seasons of dry and rainy was considered. A Self-Adaptive Differential Evolution (SADE) was used to solve the economic load dispatch problem that considers the valve-point effects and the transmission losses subject to many constraints. The results obtained were compared with those obtained using the "standard" Differential Evolution (DE), Genetic Algorithm (GA), and traditional Gradient Descent method. The results of the SADE obtained when compared with the GA, DE, and Gradient descent show the superiority of SADE over all the other methods. The research work shows that the wind energy is available in commercial quantity for generation of electricity in Nigeria. And, if tapped would help reduce the gap between the demand and supply of electricity in the country. It was also demonstrated that the wind energy integration into the power systems affects the generators total production cost