Iain MacGill

Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization

Interruptible loads represent highly valuable demand side resources within the electricity industry. However, maximizing their potential value in terms of system security and scheduling is a considerable challenge because of their widely varying and potentially complex operational characteristics. This paper investigates the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 h. The scheduling objective is to achieve a system requirement of total hourly curtailments while satisfying the operational constraints of the available interruptible loads, minimizing the total payment to them and minimizing the frequency of interruptions imposed upon them. This multiobjective optimization problem was simplified by using a single aggregate objective function. The BPSO algorithm proved capable of achieving near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem. The effectiveness of the approach was further improved by dividing the swarm into several subswarms. The proposed scheduling technique demonstrated useful performance for a relatively challenging scheduling task, and would seem to offer some potential advantages in scheduling significant numbers of widely varied and technically complex interruptible loads.

Improved energy services provision through the intelligent control of distributed energy resources

There is a need to improve the delivery of energy services, and utilizing distributed energy resources offers significant potential. We propose an energy service modeling technique that would capture temporal variations of its demand and value, and differentiate it from the electric energy consumed by the end-use equipment. We then use this technique with a novel energy service simulation platform that aims to maximize the net benefit derived from energy services. The simulation platform creates a strategy for how available distributed resources should be operated in order to provide the desired energy services while minimizing the cost of consumption. The corresponding optimization problem is solved using particle swarm optimization. The simulation platform proved capable of creating an operation schedule that maximizes net benefit under a range of challenging conditions.

Comparison of Wind Energy Support Policy and Electricity Market Design in Europe, the United States, and Australia

This paper is intended to fill a gap in the current literature comparing and contrasting the experience of a number of European countries, U.S. states, and Australia with regard to wind energy support policy and electricity market design. As wind penetrations increase, the nature of these arrangements becomes an increasingly important determinant of how effectively and efficiently this generation is integrated into the electricity industry. The jurisdictions considered in this paper exhibit a range of wind support policy measures from feed-in tariffs to green certificates, and electricity industry arrangements including vertically integrated utilities, bilateral trading with net pools, as well as gross wholesale pool markets. We consider the challenges that various countries and states have faced as wind generation expanded and how they have responded. Findings include the limitations of traditional feed-in tariffs at higher wind penetrations because they shield wind project developers and operators from the implications of their generation on wider electricity market operation. With regard to market design, wind forecasting and predispatch requirements are particularly important for forward markets, whereas the formal involvement of wind in scheduling and ancillary services (balancing and contingencies) is key for real-time markets.

Robust scheduling of residential distributed energy resources using a novel energy service decision-support tool

This paper describes a methodology for making robust day-ahead operational schedules for controllable residential distributed energy resources (DER) using a novel energy service decision support tool. The tool is based on the consumers deriving benefit from energy services and not on electric energy. It maximizes consumer net benefit by scheduling the operation of DER. The robust schedule is derived using a stochastic programming approach formulated for the DER scheduler: the objective function describing the consumer net benefit is maximized over a set of scenarios that model the range of uncertainty. The optimal scenario set is derived using heuristic scenario reduction techniques. Robust operational schedules are formulated for a ‘smart’ home case study with four controllable DER under stochastic energy service demand, availability of storage DER, and status of dynamic peak pricing. The robust schedule results in a lower expected cost but at the expense of long computation times. The computation period however is not much of a disadvantage because schedules are computed off-line. The consumer can prepare several DER schedules and simply choose the one to implement according to their perception of the coming day. The robust schedules are formulated using an improved version of co-evolutionary particle swarm optimization.

The fall (and rise) of carbon pricing in Australia: a political strategy analysis of the carbon pollution reduction scheme

In April 2010, Prime Minister Kevin Rudd announced the deferral of his flagship climate-change policy, the Carbon Pollution Reduction Scheme, after it twice failed to gain the support of the Australian Senate. The decision contributed to the curtailment of Rudds premiership and confirmed climate change as one of the most toxic issues in Australian politics. Although deficits in policy design and structural obstacles caused by Australias carbon-intensive economy were major obstacles for the Carbon Pollution Reduction Scheme, it could have passed into legislation had more effective political strategies been used to counter political opposition. A policy network framework is used to explore these political obstacles and how alternative political strategies may help to counter political obstacles to and public concern about new climate policies. In conclusion, the wider merits of policy network and political strategy approaches for the analysis of national climate politics are considered.

Distributional Effects of the Australian Renewable Energy Target (RET) through Wholesale and Retail Electricity Price Impacts

The Australian Renewable Energy Target (RET) has spurred considerable investment in renewable electricity generation, notably wind power, over the past decade. This paper considers distributional implications of the RET for different electricity customers. Using time-series regression, we show that the increasing amount of wind energy has placed considerable downward pressure on wholesale electricity prices through the so-called merit order effect. On the other hand, RET costs are passed on to consumers in the form of retail electricity price premiums. Our findings highlight likely significant redistributive transfers between different energy user classes under current RET arrangements. In particular, some energy-intensive industries are benefiting from lower wholesale electricity prices whilst being largely exempted from contributing to the costs of the scheme. By contrast, many households are paying significant RET pass through costs whilst not necessarily benefiting from lower wholesale prices. A more equitable distribution of RET costs and benefits could be achieved by reviewing the scope and extent of industry exemptions and ensuring that methodologies to estimate wholesale price components in regulated electricity tariffs reflect more closely actual market conditions. More generally, these findings support the growing international appreciation that policy makers need to integrate distributional assessments into policy design and implementation.

Impact of Electric Vehicles and Solar PV on Future Generation Portfolio Investment

This study assesses the impact of electric vehicle (EV) uptake and large-scale photovoltaic (PV) investment on the economics of future electricity-generation portfolios. A Monte-Carlo-based portfolio modeling tool was used to assess the expected overall industry cost, associated cost uncertainty, and CO2 emissions of future generation portfolios, where both EVs and PV generation have achieved major deployment. The Australian National Electricity Market (NEM) was used as a case study under uncertain future fuel and carbon prices, electricity demand, and plant capital costs. Two EV charging scenarios were considered: 1)unmanaged charging which commences immediately as the EVs arrive at suitable charging infrastructure and 2)managed charging where EV charging loads are managed so that they better align with PV output. Results show that there are potentially valuable synergies between PV generation and EV charging demand in minimizing future electricity industry costs, cost uncertainties, and emissions, particularly when EV charging loads can be managed. The value of PV generation and managed EV charging is greater for higher EV fleet size and moderate carbon prices.

Considering technology within the UN climate change negotiations

The treatment of technology within the UNFCCC negotiation process has moved from a relatively marginal subject to one of central importance, and is likely to be critical to ensuring a successful outcome at COP15. The development, deployment and transfer of low-carbon technologies, and overcoming related investment challenges, is, however, an issue considerably wider than the remit and scope of the UNFCCC. Hence there is a need to understand how cooperative action on technology under the Convention can be most effective. The existing UNFCCC technology framework would need to be significantly refined and augmented in order to appropriately address the scale and pace of the low-carbon technology implementation challenge. This paper considers the contours of an enhanced technology framework that could contribute to a future climate change agreement. In doing so, it synthesises aspects of the relevant literature and creates a link to the decision-making process of the UNFCCC. Disclaimer: The content and views expressed in this paper are not attributable to national or organisational positions in the climate negotiations or elsewhere. All of the authors contributed in their personal capacities. Corresponding Author: Morgan Bazilian, 29-31 Adelaide Rd., Dublin 02, Ireland; Phone: +353 1 678 2026; E-mail: Morgan.Bazilian@dcenr.gov.ie

The value of accurate forecasts and a probabilistic method for robust scheduling of residential distributed energy resources

We describe a decision-support tool that optimizes the energy services of residential end-users by scheduling the operation of available distributed energy resources. We discuss the application of the tool to a ‘smart’ home case study and the solution to the resulting highly-dimensional scheduling problem. We then use the optimal schedules formulated by the tool to determine the value of the forecasted information used when the schedules are created. This is achieved by computing the additional costs avoided by the end-users due to the accuracy of the forecasts. We also demonstrate how to use the tool to derive robust schedules when the end-users are not certain on the magnitude of solar insolation, magnitude of energy service demands, availability of a plug-in hybrid vehicle as storage, and status of Critical Peak Pricing. The robust schedule is derived by maximizing the expected net benefit when the schedule is applied to all likely scenario outcomes.