Afzal S. Siddiqui

Optimal Technology Selection and Operation of Commercial-Building Microgrids

The deployment of small ( < 1-2 MW ) clusters of generators, heat and electrical storage, efficiency investments, and combined heat and power (CHP) applications (particularly involving heat-activated cooling) in commercial buildings promises significant benefits but poses many technical and financial challenges, both in system choice and its operation; if successful, such systems may be precursors to widespread microgrid deployment. The presented optimization approach to choosing such systems and their operating schedules uses Berkeley Labs Distributed Energy Resources Customer Adoption Model (DER-CAM), extended to incorporate electrical and thermal storage options. DER-CAM chooses annual energy bill minimizing systems in a fully technology-neutral manner. An illustrative example for a hypothetical San Francisco hotel is reported. The chosen system includes one large reciprocating engine and an absorption chiller providing an estimated 11% cost savings and 8% carbon emission reductions under idealized circumstances.

Effect of Heat and Electricity Storage and Reliability on Microgrid Viability:A Study of Commercial Buildings in California and New York States

E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Effect of Heat and Electricity Storage and Reliability on Microgrid Viability: A Study of Commercial Buildings in California and New York States Michael Stadler, Chris Marnay, Afzal Siddiqui, Judy Lai, Brian Coffey, and Hirohisa Aki Environmental Energy Technologies Division Revised March 2009 http://eetd.lbl.gov/EA/EMP/emp-pubs.html The work described in this paper was funded by the Office of Electricity Delivery and Energy Reliability, Renewable and Distributed Systems Integration Program in the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231.

Real options analysis of investment in carbon capture and sequestration technology

Among a comprehensive scope of mitigation measures for climate change, CO2 capture and sequestration (CCS) plays a potentially significant role in industrialised countries. In this paper, we develop an analytical real options model that values the choice between two emissions-reduction technologies available to a coal-fired power plant. Specifically, the plant owner may decide to invest in either full CCS (FCCS) or partial CCS (PCCS) retrofits given uncertain electricity, CO2, and coal prices. We first assess the opportunity to upgrade to each technology independently by determining the option value of installing a CCS unit as a function of CO2 and fuel prices. Next, we value the option of investing in either FCCS or PCCS technology. If the volatilities of the prices are low enough, then the investment region is dichotomous, which implies that for a given fuel price, retrofitting to the FCCS (PCCS) technology is optimal if the CO2 price increases (decreases) sufficiently. The numerical examples provided in this paper using current market data suggest that neither retrofit is optimal immediately. Finally, we observe that the optimal stopping boundaries are highly sensitive to CO2 price volatility.

Distributed energy resources customer adoption modeling with combined heat and power applications

In this report, an economic model of customer adoption of distributed energy resources (DER) is developed. It covers progress on the DER project for the California Energy Commission (CEC) at Berkeley Lab during the period July 2001 through Dec 2002 in the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. CERTS has developed a specific paradigm of distributed energy deployment, the CERTS Microgrid (as described in Lasseter et al. 2002). The primary goal of CERTS distributed generation research is to solve the technical problems required to make the CERTS Microgrid a viable technology, and Berkeley Labs contribution is to direct the technical research proceeding at CERTS partner sites towards the most productive engineering problems. The work reported herein is somewhat more widely applicable, so it will be described within the context of a generic microgrid (mGrid). Current work focuses on the implementation of combined heat and power (CHP) capability. A mGrid as generically defined for this work is a semiautonomous grouping of generating sources and end-use electrical loads and heat sinks that share heat and power. Equipment is clustered and operated for the benefit of its owners. Although it can function independently of the traditional power system, or macrogrid, the mGrid is usually interconnected and exchanges energy and possibly ancillary services with the macrogrid. In contrast to the traditional centralized paradigm, the design, implementation, operation, and expansion of the mGrid is meant to optimize the overall energy system requirements of participating customers rather than the objectives and requirements of the macrogrid.

Optimal investment under operational flexibility, risk aversion, and uncertainty

Traditional real options analysis addresses the problem of investment under uncertainty assuming a risk-neutral decision maker and complete markets. In reality, however, decision makers are often risk averse and markets are incomplete. We confirm that risk aversion lowers the probability of investment and demonstrate how this effect can be mitigated by incorporating operational flexibility in the form of embedded suspension and resumption options. Although such options facilitate investment, we find that the likelihood of investing is still lower compared to the risk-neutral case. Risk aversion also increases the likelihood that the project will be abandoned, although this effect is less pronounced. Finally, we illustrate the impact of risk aversion on the optimal suspension and resumption thresholds and the interaction among risk aversion, volatility, and optimal decision thresholds under complete operational flexibility.

Modeling of customer adoption of distributed energy resources

This report describes work completed for the California Energy Commission (CEC) on the continued development and application of the Distributed Energy Resources Customer Adoption Model (DER-CAM). This work was performed at Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) between July 2000 and June 2001 under the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. Our research on distributed energy resources (DER) builds on the concept of the microgrid ({mu}Grid), a semiautonomous grouping of electricity-generating sources and end-use sinks that are placed and operated for the benefit of its members. Although a {mu}Grid can operate independent of the macrogrid (the utility power network), the {mu}Grid is usually interconnected, purchasing energy and ancillary services from the macrogrid. Groups of customers can be aggregated into {mu}Grids by pooling their electrical and other loads, and the most cost-effective combination of generation resources for a particular {mu}Grid can be found. In this study, DER-CAM, an economic model of customer DER adoption implemented in the General Algebraic Modeling System (GAMS) optimization software is used, to find the cost-minimizing combination of on-site generation customers (individual businesses and a {mu}Grid) in a specified test year. DER-CAMs objective is to minimize the cost of supplying electricity to a specific customer by optimizing the installation of distributed generation and the self-generation of part or all of its electricity. Currently, the model only considers electrical loads, but combined heat and power (CHP) analysis capability is being developed under the second year of CEC funding. The key accomplishments of this years work were the acquisition of increasingly accurate data on DER technologies, including the development of methods for forecasting cost reductions for these technologies, and the creation of a credible example California {mu}Grid for use in this study and in future work. The work performed during this year demonstrates the viability of DER-CAM and of our approach to analyzing adoption of DER.

Efficiency of the New York independent system operator market for transmission congestion contracts

The physical nature of electricity generation and deliverycreates special problems for the design of efficient markets, notably theneed to manage delivery in real time and the volatile congestion andassociated costs that result. Proposals for the operation of thederegulated electricity industry tend towards one of two paradigms:centralized and decentralized. Transmission congestion management can beimplemented in the more centralized point-to-point approach, a in NewYork state, where derivative transmission congestion contracts (TCCs) aretraded, or in the more decentralized flowgate-based approach. While it iswidely accepted that theoretically TCCs have attractive properties ashedging instruments against congestion cost uncertainty, whetherefficient markets for them can be established in practice has beenquestioned. Based on an empirical analysis of publicly available datafrom years 2000 and 2001, it appears that New York TCCs providedmarketparticipants with a potentially effective hedge against volatilecongestion rents. However, the prices paid for TCCs systematicallydiverged from the resulting congestion rents for distant locations and athigh prices. The price paid for the hedge not being in line with thecongestion rents, i.e. unreasonably high risk premiums are being paid,suggests an inefficient market. The low liquidity of TCC markets and thedeviation of TCC feasibility requirements from actual energy flows arepossible explanations.

Risk Averse Scheduling by a PEV Aggregator Under Uncertainty

Research on electric power systems has considered the impact of foreseeable plug-in electric vehicle (PEV) penetration on its regulation, planning, and operation. Indeed, detailed treatment of PEV charging is necessary for efficient allocation of resources. It is envisaged that a coordinator of charging schedules, i.e., a PEV aggregator, could exercise some form of load control according to electricity market prices and network charges. In this context, it is important to consider the effects of uncertainty of key input parameters to optimization algorithms for PEV charging schedules. However, the modeling of the PEV aggregators exposure to profit volatility has received less attention in detail. Hence, this paper develops a methodology to maximize PEV aggregator profits taking decisions in day-ahead and balancing markets while considering risk aversion. Under uncertain market prices and fleet mobility, the proposed two-stage linear stochastic program finds optimal PEV charging schedules at the vehicle level. A case study highlights the effects of including the conditional value-at-risk (CVaR) term in the objective function and calculates two metrics referred to as the expected value of aggregation and flexibility.

Optimal Planning and Operation of Smart Grids with Electric Vehicle Interconnection

Connection of electric storage technologies to smartgrids will have substantial implications for building energy systems. Local storage will enable demand response. When connected to buildings, mobile storage devices such as electric vehicles (EVs) are in competition with conventional stationary sources at the building. EVs can change the financial as well as environmental attractiveness of on-site generation (e.g. PV or fuel cells). In order to examine the impact of EVs on building energy costs and CO2 emissions, a distributed-energy-resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO2 emissions and solved for 2020 technology assumptions. The mixedinteger linear program is applied to a set of 139 different commercial buildings in California and example results as well as the aggregated economic and environmental benefits are reported. Special constraints for the available PV, solar thermal, and EV parking lots at the commercial buildings are considered. The research shows that EV batteries can be used to reduce utility-related energy costs at the smart grid or commercial building due to arbitrage of energy between buildings with different tariffs. However, putting more emphasis on CO2 emissions makes stationary storage more attractive and stationary storage capacities increase while the attractiveness of EVs decreases. The limited availability of EVs at the commercial building decreases the attractiveness of EVs and if PV is chosen by the optimization, then it is mostly used to charge the stationary storage at the commercial building and not the EVs connected to the building.

Transmission Capacity between Norway and Germany - a Real Options Analysis

Interconnection of two electricity markets provides revenues to the owner of the line. In this paper we study the alternatives open to an investor holding a unique right to construct transmission c ...