Abstract: The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity James H. Williams, 1,2 Andrew DeBenedictis, 1 Rebecca Ghanadan, 1,3 Amber Mahone, 1 Jack Moore, 1 William R. Morrow III, 4 Snuller Price, 1 Margaret S. Torn 3 * Several states and countries have adopted targets for deep reductions in greenhouse gas emissions by 2050, but there has been little physically realistic modeling of the energy and economic transformations required. We analyzed the infrastructure and technology path required to meet California’s goal of an 80% reduction below 1990 levels, using detailed modeling of infrastructure stocks, resource constraints, and electricity system operability. We found that technically feasible levels of energy efficiency and decarbonized energy supply alone are not sufficient; widespread electrification of transportation and other sectors is required. Decarbonized electricity would become the dominant form of energy supply, posing challenges and opportunities for economic growth and climate policy. This transformation demands technologies that are not yet commercialized, as well as coordination of investment, technology development, and infrastructure deployment. n 2004, Pacala and Socolow (1) proposed a way to stabilize climate using existing green- house gas (GHG) mitigation technologies, vi- sualized as interchangeable, global-scale “wedges” of equivalent emissions reductions. Subsequent work has produced more detailed analyses, but none combines the sectoral granularity, physical and resource constraints, and geographic scale needed for developing realistic technology and policy roadmaps (2–4). We addressed this gap by analyzing the specific changes in infrastructure, technology, cost, and governance required to de- carbonize a major economy, at the state level, that has primary jurisdiction over electricity supply, transportation planning, building standards, and other key components of an energy transition. California is the world’s sixth largest econ- omy and 12th largest emitter of GHGs. Its per capita GDP and GHG emissions are similar to those of Japan and western Europe, and its policy and technology choices have broad rele- vance nationally and globally (5, 6). California’s Assembly Bill 32 (AB32) requires the state to reduce GHG emissions to 1990 levels by 2020, a reduction of 30% relative to business-as-usual assumptions (7). Previous modeling work we per- formed for California’s state government formed the analytical foundation for the state’s AB32 implementation plan in the electricity and natural gas sectors (8, 9). California has also set a target of reducing 2050 emissions 80% below the 1990 level, con- I Energy and Environmental Economics, 101 Montgomery Street, Suite 1600, San Francisco, CA 94104, USA. 2 Monterey Institute of International Studies, 460 Pierce Street, Monterey, CA 93940, USA. 3 Energy and Resources Group, University of Cali- fornia,& Earth Sciences Division, Lawrence Berkeley National Laboratory (LBNL),, Berkeley, CA 94720, USA. 4 Environmental Energy Technologies Division, LBNL, Berkeley, CA 94720, USA. *To whom correspondence should be addressed. E-mail: mstorn@lbl.gov sistent with an Intergovernmental Panel on Cli- mate Change (IPCC) emissions trajectory that would stabilize atmospheric GHG concentrations at 450 parts per million carbon dioxide equivalent (CO 2 e) and reduce the likelihood of dangerous an- thropogenic interference with climate (10). Work- ing at both time scales, we found a pressing need for methodologies that bridge the analytical gap between planning for shallower, near-term GHG reductions, based entirely on existing commercialized technology, and deeper, long-term GHG reduc- tions, which will depend substantially on technol- ogies that are not yet commercialized. We used a stock-rollover methodology that simulated physical infrastructure at an aggregate level, and built scenarios to explore mitigation options (11, 12). Our model divided California’s economy into six energy demand sectors and two energy supply sectors, plus cross-sectoral eco- nomic activities that produce non-energy and non-CO 2 GHG emissions. The model adjusted the infrastructure stock (e.g., vehicle fleets, build- ings, power plants, and industrial equipment) in each sector as new infrastructure was added and old infrastructure was retired, each year from 2008 to 2050. We constructed a baseline scenario from government forecasts of population and gross state product, combined with regression-based infra- structure characteristics and emissions intensities, producing a 2050 emissions baseline of 875 Mt CO 2 e (Fig. 1). In mitigation scenarios, we used backcasting, setting 2050 emissions at the state target of 85 Mt CO 2 e as a constrained outcome, and altered the emissions intensities of new in- frastructure over time as needed to meet the tar- get, employing 72 types of physical mitigation measures (13). In the short term, measure selec- tion was driven by implementation plans for AB32 and other state policies (table S1). In the long term, technological progress and rates of in- troduction were constrained by physical feasi- bility, resource availability, and historical uptake rates rather than relative prices of technology, en- ergy, or carbon as in general equilibrium models (14). Technology penetration levels in our model are within the range of technological feasibility for the United States suggested by recent assess- ments (table S20) (15, 16). We did not include technologies expected to be far from commercial- ization in the next few decades, such as fusion- based electricity. Mitigation cost was calculated as the difference between total fuel and measure costs in the mitigation and baseline scenarios. Our fuel and technology cost assumptions, including learning curves (tables S4, S5, S11, and S12, and fig. S29), are comparable to those in other recent studies (17). Clearly, future costs are very uncertain over such a long time horizon, especially for technologies that are not yet commercialized. We did not assume explicit life-style changes (e.g., vegetarianism, bicycle transportation), which could have a substantial effect on mitigation requirements and costs (18); behavior change in our model is subsumed within conservation measures and en- ergy efficiency (EE). To ensure that electricity supply scenarios met the technical requirements for maintaining reli- able service, we included an electricity system dispatch algorithm that tested grid operability. Without a dispatch model, it is difficult to de- termine whether a generation mix has infeasibly high levels of intermittent generation. We devel- oped an electricity demand curve bottom-up from sectoral demand, by season and time of day. On the basis of the demand curve, the model con- strained generation scenarios to satisfy in succes- sion the energy, capacity, and system-balancing requirements for reliable operation. The operabil- ity constraint set physical limits on the penetra- tion of different types of generation and specified the requirements for peaking generation, on-grid energy storage, transmission capacity, and out-of- state imports and exports for a given generation mix (table S13 and figs.S20 to S31). It was as- sumed that over the long run, California would not “go it alone” in pursuing deep GHG reduc- tions, and thus that neighboring states would de- carbonize their generation such that the carbon intensity of imports would be comparable to that of California in-state generation (19). Electrification required to meet 80% reduc- tion target. Three major energy system transfor- mations were necessary to meet the target (Fig. 2). First, EE had to improve by at least 1.3% year −1 over 40 years. Second, electricity supply had to be nearly decarbonized, with 2050 emissions in- tensity less than 0.025 kg CO 2 e/kWh. Third, most existing direct fuel uses had to be electrified, with electricity constituting 55% of end-use energy in 2050 versus 15% today. Results for a mitigation scenario, including these and other measures, are shown in Fig. 1. Of the emissions reductions relative to 2050 baseline emissions, 28% came from EE, 27% from decarbonization of electricity generation, 14% from a combination of energy

Abstract: The environmental and economical merits of converting solar energy into electricity via photovoltaic cells have caused an ever increasing interest among developed and developing countries to allocate more budget on photovoltaic systems in order to boost up their efficiency in recent years. Besides the material and design parameters, there are several omnipresent factors such as dust, humidity and air velocity that can influence the PV cell's performance. There have been a handful of studies conducted on the effect of various influential parameters on the efficiency and performance of photovoltaic cells; however none has taken all these three parameters into account simultaneously. In this study the impact of dust accumulation, humidity level and the air velocity will be elaborated separately and finally the impact of each on the other will be clarified. It is shown that each of these three factors affect the other two and it is concluded that in order to have a profound insight of solar cell design, the effect of these factors should be taken into consideration in parallel.

Abstract: The commercial development and current economic incentives associated with energy storage using redox flow batteries (RFBs) are summarised. The analysis is focused on the all-vanadium system, which is the most studied and widely commercialised RFB. The recent expiry of key patents relating to the electrochemistry of this battery has contributed to significant levels of commercialisation in, for example, Austria, China and Thailand, as well as pilot-scale developments in many countries. The potential benefits of increasing battery-based energy storage for electricity grid load levelling and MW-scale wind/solar photovoltaic-based power generation are now being realised at an increasing level. Commercial systems are being applied to distributed systems utilising kW-scale renewable energy flows. Factors limiting the uptake of all-vanadium (and other) redox flow batteries include a comparatively high overall internal costs of $217 kW−1 h−1 and the high cost of stored electricity of ≈ $0.10 kW−1 h−1. There is also a low-level utility scale acceptance of energy storage solutions and a general lack of battery-specific policy-led incentives, even though the environmental impact of RFBs coupled to renewable energy sources is favourable, especially in comparison to natural gas- and diesel-fuelled spinning reserves. Together with the technological and policy aspects associated with flow batteries, recent attempts to model redox flow batteries are considered. The issues that have been addressed using modelling together with the current and future requirements of modelling are outlined. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract: Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Abstract: Advances in the development of electric vehicles, along with policy incentives, will see a wider uptake of this technology in the transport sector in future years. However, the widespread adoption of electric vehicles could lead to adverse effects on the power system, especially for existing distribution networks. These effects would include excessive voltage drops and overloading of network components, which occur mainly during periods of simultaneous charging of large numbers of electric vehicles. This paper demonstrates how controlling the rate at which electric vehicles charge can lead to better utilization of existing networks. A technique based on linear programming is employed, which determines the optimal charging rate for each electric vehicle in order to maximize the total power that can be delivered to the vehicles while operating within network limits. The technique is tested on a section of residential distribution network. Results show that, by controlling the charging rate of individual vehicles, high penetrations can be accommodated on existing residential networks with little or no need for upgrading network infrastructure.

Abstract: Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the other hand, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations, and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

Abstract: The restructuring of energy markets has increased the concern about the existing interdependency between the primary energy supply and electricity networks, which are analyzed traditionally as independent systems. The aim of this paper is focused on an integrated formulation for the steady-state analysis of electricity and natural gas coupled systems considering the effect of temperature in the natural gas system operation and a distributed slack node technique in the electricity network. A general approach is described to execute a single gas and power flow analysis in a unified framework based on the Newton-Raphson formulation. The applicability of the proposed approach is demonstrated by analyzing the Belgian gas network combined with the IEEE-14 test system and a 15-node natural gas network integrated with the IEEE-118 test system.

Abstract: In this paper, the coordinated integration of aggregated plug-in electric vehicle (PEV) fleets and renewable energy sources (wind energy) in power systems is studied by stochastic security-constrained unit commitment (Stochastic SCUC) model, which minimizes the expected grid operation cost while considering the random behavior of the many PEVs. PEVs are mobile and distributed devices with deferrable options for the supply/utilization of energy at various times and locations. The increased utilization of PEVs, which consume electricity rather than fossil fuel for driving, offers unique economic and environmental opportunities, and brings out new challenges to electric power system operation and planning. The storage capability of PEVs could help power systems mitigate the variability of renewable energy sources and reduce grid operation costs. Vehicle-to-grid (V2G) enables PEVs to have bi-directional power flows once they are connected to the grid, i.e., they can either inject power to, and draw power from, the grid which adds further complexity to power system operations. PEVs signify customers' random behavior when considering their driving patterns, locational energy requirements, topological grid interconnections, and other constraints imposed by the consumers. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of PEVs on the grid operation cost and hourly wind energy dispatch.

Abstract: Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the energy consumption to periods with low electricity prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according to production is crucial. We present a model for a house with a ground source based heat pump used for supplying thermal energy to a water based floor heating system. The model is a linear state space model and the resulting controller is an Economic MPC formulated as a linear program. The model includes forecasts of both weather and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25–35% of the electricity cost.

Abstract: Electric vehicle (EV) numbers are expected to significantly increase in the coming years reflecting their potential to reduce air pollutants and greenhouse gas emissions. Charging such vehicles will impose additional demands on the electricity network but given the pattern of vehicle usage, the possibility exists to discharge the stored energy back to the grid when required, for example when lower than expected wind generation is available. Such vehicle-to-grid operation could see vehicle owners supplying the grid if they are rewarded for providing such services. This paper describes a model of an electric vehicle storage system integrated with a standardized power system (the IEEE 30-node power system model). A decision-making strategy is established for the deployment of the battery energy stored, taking account of the state of charge, time of day, electricity prices and vehicle charging requirements. Applying empirical data, the benefits to the network in terms of load balancing and the energy and cost savings available to the vehicle owner are analyzed. The results show that for the case under study, the EVs have only a minor impact on the network in terms of distribution system losses and voltage regulation but more importantly the vehicle owner's costs are roughly halved.

Abstract: As both a regulator and an academic, Fred Kahn argued that end-use electricity consumers should face prices that reflect the time-varying marginal costs of generating electricity. This has been very slow to happen in the US, even in light of recent technological advances that have lowered costs and improved functionality for meters and automated demand response technologies. We describe these recent developments and discuss the remaining barriers to the proliferation of time-varying electricity pricing.

Abstract: An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.

Abstract: Monitoring electricity consumption in the home is an important way to help reduce energy usage and Non-Intrusive Load Monitoring (NILM) techniques are a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. In this paper, we discuss event detection algorithms used in the NILM literature and propose new metrics for evaluating them. In particular, we introduce metrics that incorporate information contained in the power signal instead of strict detection rates. We show that this information is important for NILM applications with the goal of improving appliance energy disaggregation. Our work was carried out on a publicly-available week-long dataset of real residential power usage.

Abstract: In this paper, we develop a methodology for estimating marginal emissions of electricity demand that vary by location and time of day across the United States. The approach takes account of the generation mix within interconnected electricity markets and shifting load profiles throughout the day. Using data available for 2007 through 2009, with a focus on carbon dioxide (CO2), we find substantial variation among locations and times of day. Marginal emission rates are more than three times as large in the upper Midwest compared to the western United States, and within regions, rates for some hours of the day are more than twice those for others. We apply our results to an evaluation of plug-in electric vehicles (PEVs). The CO2 emissions per mile from driving PEVs are less than those from driving a hybrid car in the western United States and Texas. In the upper Midwest, however, charging during the recommended hours at night implies that PEVs generate more emissions per mile than the average car currently on the road. Underlying many of our results is a fundamental tension between electricity load management and environmental goals: the hours when electricity is the least expensive to produce tend to be the hours with the greatest emissions. In addition to PEVs, we show how our estimates are useful for evaluating the heterogeneous effects of other policies and initiatives, such as distributed solar and real-time pricing.

Abstract: Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a “smart grid” by incorporating extensive information and communication infrastructures. Key features in such a “smart grid” include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.

Abstract: Electric mobility is considered as a promising option for future individual transportation in terms of lower CO2-emissions and reduced dependence on fossil fuels. In order to analyze its impacts effectively, an agent based model is proposed. It integrates three domains which are mainly affected by electric mobility. Vehicle fleet evolution and vehicle energy demand simulations are combined with a transportation simulation, thus determining the daily behavior of electric vehicles and providing individual battery energy levels at the different locations of the vehicles during the day. Further, a power system model combined with a charging control algorithm is included in order to study general effects in electricity networks and to provide insights into new electric vehicle load patterns, as well as into changes in transport behavior. It is shown that network congestion can be mitigated using control signals. The paper describes the method and the integration of the three different domains and shows results of the integrated analysis tool.

Abstract: SUMMARY The foreseeable increase in the use of electric vehicles (EV) led to the discussion on intermediate entities that could help manage a great number of EV. An aggregation agent for electric vehicles is a commercial middleman between a system operator (SO) and plug-in EV. From the SO perspective, the aggregator is seen as a large source of generation or load, which could provide ancillary services such as spinning and regulating reserve. Generally, these services will be provided in the day-ahead and intraday electricity markets. In addition, the aggregator also participates in the electricity market with supply and demand energy bids. This paper provides a comprehensive bibliographic survey on the aggregator role in the power system operation and electricity market. The scope of the survey covers 59 references divided in journal, conference proceedings, thesis, research papers, and technical reports published after 1994. These papers are put into several technical categories: electricity market and EV technical and economic issues; aggregation agent concept, role and business model; algorithms for EV management as a load/resource. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract: Plug-in electric vehicles (PEVs) are expected to balance the fluctuation of renewable energy sources (RES). To investigate the contribution of PEVs, the availability of mobile battery storage and the control mechanism for load management are crucial. This study therefore combined the following: a stochastic model to determine mobility behavior, an optimization model to minimize vehicle charging costs and an agent-based electricity market equilibrium model to estimate variable electricity prices. The variable electricity prices are calculated based on marginal generation costs. Hence, because of the merit order effect, the electricity prices provide incentives to consume electricity when the supply of renewable generation is high. Depending on the price signals and mobility behavior, PEVs calculate a cost minimizing charging schedule and therefore balance the fluctuation of RES. The analysis shows that it is possible to limit the peak load using the applied control mechanism. The contribution of PEVs to improving the integration of intermittent renewable power generation into the grid depends on the characteristic of the RES generation profile. For the German 2030 scenario used here, the negative residual load was reduced by 15–22% and the additional consumption of negative residual load was between 34 and 52%.