When Average isn't Good Enough: Cogeneration Evaluation and the Value of Electricity

As utilities nationwide plan to increase their sustainable practices into the future, they are evaluating various methods to maximize beneficial use of their resources, especially biogas. Such biogas utilization programs primarily consist of boilers or cogeneration but can also be coupled with wind or solar programs. In Fairfield, CA, the Fairfield-Suisun Sewer District (District) plant is already an industry leader by employing biogas cogeneration, solar photovoltaics (PV), and wind turbine sources to generate renewable energy and produce the majority of its electricity use onsite. When looking to replace systems and further advance their energy program, the traditional simple business case evaluation was not sufficient and new analysis tools and methods were required. The District treats an average of 14 million gallons per day of wastewater collected from its sewer shed. Plant influent is a combination of both domestic and industrial sources. The District has two mesophilic anaerobic digesters that process the primary sludge and waste activated sludge. Furthermore, the plant loads approximately 8,200 gallons per day of a mix of candy waste and dairy sludge to its digesters as part of the high-strength waste (HSW) program to enhance the digesters' energy yield that generates revenues at the plant. the District currently operates an aging 900 kilowatts (kW) cogeneration engine. Recently Brown and Caldwell (BC) completed a Digester Gas Utilization Master Plan for the District. This analysis faced many complicating factors: - Multiple electrical generating system, including existing net-metered solar PV - Pacific Gas & Electric (PG&E) time-of-use rate schedule - Other potential electric tariffs, such as bioenergy market adjusting tariff (BioMAT), net energy metering (NEM2), and net energy metering multiple tariff (NEM-MT) - Standby charges for new installed capacity - Grant funding opportunities - Variable facility electric load The core of any financial energy evaluation is the value of produced electricity. A behind the meter energy production system offsets imported utility power and derives value by reducing the electric bill. With electric rate schedules growing more complicated and relying on time-of-use rates and larger demand charges, calculating a more accurate estimate of the value of produced electricity requires a much more in depth and comprehensive analysis & the average cost of electricity using 12 months of utility bills is often misleading. The method BC developed to size and evaluate the benefits from a new cogeneration system included: - Minute by minute data for digester gas production - Solar and wind generation profiles - Facility load electricity usage profiles - Variable quantities of HSW and resulting digester gas production - Natural gas supplemental fuel to the engine system - Medium- and low-pressure digester gas storage - Internal combustion engine output based on part load efficiency - Hourly electricity generation, use and import/export calculations - Costs and value derived from the energy system This manuscript will present the methodology used to complete this novel approach to engine evaluation as well as the benefits and drawbacks to this approach relative to the industry standard method for engine evaluation. Finally, this manuscript will discuss the economic impacts and biogas utilization, which minimized flaring while optimizing time of use with energy production, based on this analysis. The basis of design for sizing equipment is based on historical digester gas (DG) production data with additional capacity for future increases in DG produced by increased HSW receiving. Historical and anticipated DG production is presented in Table 1. This table summarizes the anticipated annual average and 90th percentile DG production. It also provides the maximum fuel requirement for the gas conditioning system design basis. Figures 1 and 2 provide a frequency analysis of the historical and anticipated ranges of DG production without and with HSW addition, respectively. As noted in Figure 2, the addition of HSW causes bimodal distribution in the frequency analysis. To perform the engine sizing analysis, the minute fluctuations observed in DG production were simulated to reflect the dynamic operating conditions of the engine(s). DG generated from HSW codigestion was simulated on a minute-by-minute basis and layered on top of the current DG production for small (70,000 gallons per week) and large (140,000 gallons per week) HSW scenarios to simulate engine power output. It was assumed that HSW deliveries only occurred during weekdays, which influence the amount of HSW available for the weekends, shown in Figure 3. Ideally HSW is fed continuously over a 24-hour period, the analysis assumed a less than perfect distribution after morning deliveries as shown in Figure 4, which is similar to the District's current HSW feed strategy. Figures 3 and 4 show simulated above average DG production from HSW during weekdays and midday. These normalized data distributions were implemented for both HSW programs and adjusted based on the incoming HSW volume. These simulated DG data were used to assess four engine size scenarios with and without natural gas blending. Additionally, these scenarios were evaluated under two different DG storage options (high and medium pressure). Based on the DG storage and utilization analysis along with the economic analysis, the District elected to move forward with the one 1,100 kW engine design. Based on the assumptions used in the analysis and the best information available at the time of the analysis, this engine design provides the greatest net present value over the range of HSW programs. This allows the District to ramp up the HSW program at a preferred rate. Additionally, the District decided that the design will not include gas storage and will rely on natural gas blending to smooth out peaks in gas production, take advantage of spark spread, and reduce flaring due to insufficient DG fuel to operate the engine at its minimum capacity.