Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

An economic evaluation of the potential for distributed energy in Australia

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently completed a major study investigating the value of distributed energy (DE; collectively demand management, energy efficiency and distributed generation) technologies for reducing greenhouse gas emissions from Australia’s energy sector (CSIRO, 2009). This comprehensive report covered potential economic, environmental, technical, social, policy and regulatory impacts that could result from the wide scale adoption of these technologies. In this paper we highlight the economic findings from the study. Partial Equilibrium modeling of the stationary and transport sectors found that Australia could achieve a present value welfare gain of around $130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050. Modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12% in 2030 and 65% in 2050 by using local resources to better cater for an evolving supply-demand imbalance. Further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market. Changes to the dispatch relative to a base case can have both positive and negative effects on network losses.Distributed energy; Economic modeling; Carbon price; Electricity markets

Optimal provision of distributed reserves under dynamic energy service preferences

We propose and solve a stochastic dynamic programming (DP) problem addressing the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy service preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove optimal policy monotonicity and value function convexity. These properties enable us to propose and implement a smart assisted value iteration (AVI) algorithm and an approximate DP (ADP) that exploits related functional approximations. Numerical results demonstrate the validity of the solution techniques and the computational advantage of the proposed ADP on realistic, large-state-space problems

A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK

This paper reviews major studies in three traditional lines of research in residential energy consumption in the UK, i.e. economic/infrastructure, behaviour, and load profiling. Based on the review the paper proposes a three-dimensional model for archetyping residential energy consumers in the UK by considering property energy efficiency levels, the greenness of household behaviour of using energy, and the duration of property daytime occupancy. With the proposed model, eight archetypes of residential energy consumers in the UK have been identified. They are: pioneer greens, follower greens, concerned greens, home stayers, unconscientious wasters, regular wasters, daytime wasters, and disengaged wasters. Using a case study, these archetypes of residential energy consumers demonstrate the robustness of the 3-D model in aiding local energy policy/intervention design in the UK

Marginal abatement cost curves (MACCs): important approaches to obtain (firm and sector) greenhouse gases (GHGs) reduction

The study aims to identify appropriate methods that can help organisations to reduce energy use and emissions by using an effective concept of sustainability. In different countries, estimates of marginal abatement costs for reducing GHG emissions have been widely used. Around the world, many researchers have focused on MACCs and reported different results. This may due to different assumptions used which in turn lead to uncertainty and inaccuracy. Under these circumstances, much attention has been paid to the need for the role of MACC in providing reliable information to decision makers and various stakeholders. By reviewing the literature, this paper has analysed MACCs in terms of the role of different approaches to MACCs, representations of MACCs, MACC applications, pricing carbon, verification, and sectors analysis for energy and emissions projections. This paper concludes that MACCs should depend on actual data to provide more reliable information that may assist (firms and sectors) stockholders to determine what appropriate method for reducing emission

Using Experiments to Foster Innovation and Improve the Effectiveness of Energy Efficiency Programs

This paper argues that the establishment of a process designed to manage innovation must be developed in California to foster the creation of needed program improvements and develop new and more effective energy efficiency delivery programs. This paper discusses several key institutional problems that must be overcome to achieve significant progress

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods