A comprehensive introduction to cost of service, revenue requirements, and rate design for electric utilities.
Electric utility ratemaking is the process of determining how much to charge customers for electricity, and how to structure those charges fairly. It's driven by two fundamental questions: How much revenue does the utility need? and How should those costs be shared among customers?
Cost of Service (COS) is a well-established, widely accepted process that determines the cost incurred by a utility in providing service to customers. Rate Design is the development of prices and pricing signals that convey the cost of service to customers. Together, they form the foundation of equitable ratemaking.
Cost of service and rate design follow an integrated process. Each step builds on the previous one, progressing from total system costs down to individual customer rates.
Determine the total cost of operating the utility — what must be recovered through rates.
Unbundle costs by function: Production, Transmission, Distribution, and Customer.
Classify each function's costs as demand-related (fixed), energy-related (variable), or customer-related.
Allocate classified costs to customer classes based on their usage characteristics and cost causation.
Design specific rate structures — customer charges, demand charges, and energy charges — to recover allocated costs.
While COS follows a structured methodology, the process involves professional judgment at every step. Adjustments must be "known and measurable," costs must be "prudent, reasonable, and necessary," and policy considerations shape how costs ultimately translate into rates.
Next: Before diving into the rate process, we need to understand how the electric system actually works — from generation to your meter.
An electric utility operates across four core functions. Understanding these functions is essential because costs are organized and allocated based on this structure.
Power plants that produce electricity. Includes fossil fuel, nuclear, renewable, and purchased power. The largest cost component for most utilities.
High-voltage lines and substations that move power from generators to distribution areas. Sized for system peak demand.
Local poles, wires, transformers, and substations delivering power to individual customers. Often the most infrastructure-intensive function.
Meters, billing, accounting, key accounts, energy efficiency programs, and customer support operations.
Electricity flows from generation sources through the transmission and distribution system to reach customers at various voltage levels. Modern grids increasingly include distributed resources like rooftop solar, battery storage, and electric vehicles.
Natural gas remains dominant but renewables are surging. Wind and solar generated a combined 17% of U.S. electricity in 2025, up from 14% in 2023 — with solar capacity additions outpacing all other sources. Source: U.S. EIA.
Nearly all new U.S. generating capacity is now solar, battery storage, or wind — reflecting both economics and Inflation Reduction Act incentives.
The Inflation Reduction Act (2022): The IRA fundamentally changed utility resource economics. Production Tax Credits (PTCs) for wind, Investment Tax Credits (ITCs) for solar and battery storage, and technology-neutral clean energy credits starting 2025 made renewable generation the lowest-cost new resource in many markets. For municipal utilities, direct-pay provisions allow tax-exempt entities to monetize these credits for the first time. The IRA's impact flows through every aspect of rate setting — from power supply costs in the revenue requirement to DER economics in rate design.
The Data Center Demand Surge: After two decades of essentially flat U.S. electricity demand, data center and AI workloads are driving unprecedented load growth. EIA projects U.S. electricity consumption will set records in 2025–2026. The International Energy Agency (IEA) projects global data center power demand could double by 2030, with EPRI estimating similar growth domestically. For utilities, this means revisiting load forecasts, accelerating generation and transmission investment, and designing rate structures for large, high-load-factor customers that may rival existing industrial classes in size.
Utility infrastructure must be sized to meet peak demand — the single highest point of electricity use. But most of the time, the system operates well below its capacity. Load factor measures how efficiently customers use the available capacity.
Monthly peak demand vs. average load illustrates the "unused" capacity that the utility must still maintain. Hover to explore.
A customer with a low load factor uses the system's fixed infrastructure intensely for short periods but contributes relatively little energy revenue. This is a core driver of cost allocation — high-peak, low-energy customers impose disproportionate infrastructure costs. Understanding load factor is essential to fair ratemaking.
Next: With the system understood, we can calculate how much total revenue the utility needs to collect — the revenue requirement.
The revenue requirement is the total amount of money the utility needs to collect through rates. It answers three fundamental questions: When should rates change? Why are they changing? And how much do they need to change?
The revenue requirement captures all costs of providing electric service, less any non-rate income sources. For municipal utilities, this is typically calculated on a cash basis, while investor-owned utilities use a utility (accrual) basis.
How a typical municipal utility's $100M revenue requirement breaks down by component.
The revenue requirement is built on a test year — either a recent historical year or a projected future year. Adjustments are applied to reflect known and measurable changes: load growth, new capital projects, contract changes, and inflation. These adjustments must be prudent, reasonable and necessary, and used and useful.
A financial forecast model projects the utility's performance over 5-10 years. It integrates load growth, power supply costs, operating budgets, capital plans, and debt service to answer when rates need to change and by how much. Scenario analysis lets stakeholders evaluate trade-offs between rate increases, debt issuances, and capital spending.
Debt Service Coverage Ratio (DSCR), Days Cash on Hand, Debt-to-Equity ratio — rating agencies and bond covenants require these be maintained at healthy levels.
What if we delay a capital project? Issue bonds now vs. later? Phase rate increases over 3 years? The financial model quantifies each option's impact on rates and financial health.
Next: Now that we know the total amount needed, how do we distribute those costs fairly? That's cost of service methodology.
Once the total revenue requirement is established, the COS study determines how to fairly distribute those costs across customer classes. This involves three sequential steps: functionalization, classification, and allocation.
Each line item in the revenue requirement is assigned to one of the four utility functions. Some costs are directly assignable (e.g., power plant maintenance clearly belongs to Production). Others, like administrative overhead, must be allocated across functions using a reasonable basis such as labor distribution.
Example: How a $100M revenue requirement breaks down across utility functions.
A budget line for "Administrative & General" ($10M) can't be directly assigned to one function. If Production accounts for 54% of direct labor, Transmission 11%, Distribution 27%, and Customer 8%, then A&G costs are allocated in those same proportions. This labor-based allocation is standard industry practice.
After functionalization, each cost is classified by its nature. This determines whether the cost should be recovered through fixed charges or variable charges in the final rate design.
| Function | Cost Classification | Fixed or Variable? | Recovered Through |
|---|---|---|---|
| Production | Demand-related (capacity costs) | Fixed | Demand charges / fixed charges |
| Energy-related (fuel, purchased power) | Variable | Energy charges ($/kWh) | |
| Transmission | Demand-related | Fixed | Demand charges |
| Distribution | Demand-related (poles, wires) | Fixed | Demand / customer charges |
| Customer-related (meters, service drops) | Fixed | Customer charges | |
| Customer Service | Customer-related | Fixed | Customer charges |
Most utility costs are fixed — they don't change whether a customer uses 100 kWh or 1,000 kWh. Yet historically, most residential revenue has been collected through variable energy charges. This mismatch creates revenue instability and cross-subsidization. Modern rate design increasingly shifts toward better alignment of fixed costs with fixed charges.
Emerging Revenue Requirement Drivers: Two forces are reshaping utility revenue requirements in the mid-2020s. The Inflation Reduction Act reduces power supply costs through clean energy tax credits and direct-pay provisions — lowering the production component of the revenue requirement. Simultaneously, data center load growth is driving new transmission and distribution investment, increasing the infrastructure component. Financial planning models must now capture both dynamics to produce realistic rate forecasts.
Next: With costs classified, we can allocate them to specific customer classes — residential, commercial, and industrial.
This is where the COS study determines what each customer class should pay. Costs are allocated based on cost causation — which customers are responsible for driving those costs.
Customers are grouped into classes based on similar size, usage patterns, voltage level, and service requirements. Common classes include Residential, Small Commercial, Large Commercial, Industrial, and Lighting.
Fixed infrastructure costs (generation capacity, transmission, distribution) are allocated based on each class's contribution to system peak demand. Classes that drive the peak pay more.
Variable costs like fuel and purchased power are allocated based on each class's total energy consumption (kWh), adjusted for system losses at each voltage level.
Costs tied to serving individual customers (meters, billing, service drops) are allocated by number of customers, often weighted by service complexity.
Example allocation showing how $100M in total costs distributes across customer classes.
The biggest analytical decision in a COS study is how to allocate demand costs. Different methods — 1 Coincident Peak (1CP), 4CP, 12CP, Non-Coincident Peak (NCP), and Average & Excess Demand (AED) — can shift millions of dollars between customer classes. The method chosen should reflect the utility's system characteristics and peaking patterns. Switch to Detailed mode to see how these methods work with actual numbers.
Consider a system with three customer classes and $100,000 in demand costs to allocate. Each method uses different data about when and how much each class demands from the system.
Select a method to see how it allocates $100,000 in demand costs. Note how the allocation shifts based on the method chosen.
All six methods compared side-by-side. The range between methods shows the sensitivity of cost allocation to methodology choices.
| Method | Class A (Residential) | Class B (Commercial) | Class C (Industrial) |
|---|---|---|---|
| 1 CP | 47.0% ($47,000) | 29.0% ($29,000) | 24.0% ($24,000) |
| 4 CP | 47.6% ($47,600) | 26.6% ($26,600) | 25.8% ($25,800) |
| 12 CP | 42.6% ($42,600) | 27.4% ($27,400) | 30.0% ($30,000) |
| 1 NCP | 44.7% ($44,651) | 28.8% ($28,837) | 26.5% ($26,512) |
| 12 NCP | 41.8% ($41,838) | 28.9% ($28,900) | 29.3% ($29,262) |
| AED | 43.9% ($43,880) | 28.7% ($28,660) | 27.5% ($27,460) |
| Range | 41.8% – 47.6% | 26.6% – 29.0% | 24.0% – 30.0% |
Class C (Industrial) shows the widest range — a ratio of 1.25 between the highest and lowest allocation. This means the choice of method alone can swing Industrial's share by 25%. For a $100M utility, that's a $6M difference. This is why the choice of allocation methodology is one of the most consequential decisions in a rate study.
Understanding the difference between coincident peak (CP), non-coincident peak (NCP), and sum-of-maximum demands is critical.
Each class's demand at the time of the system peak. This measures how much each class contributes to the peak that sizes the system. A class that peaks at a different time than the system contributes less to CP.
Each class's own maximum demand, regardless of when it occurs. This measures the infrastructure each class individually requires. The sum of NCPs always exceeds the system peak due to load diversity.
Splits demand costs into two parts: an "average" component allocated by energy use, and an "excess" component allocated by the difference between NCP and average demand. A hybrid approach.
This diagram shows the complete COS process — from total revenue requirement through functionalization, classification, and allocation to customer classes.
Next: With costs allocated to each class, the final step is designing the actual rate structure customers see on their bills.
Rate design is where the COS results become the actual prices customers pay. While the COS provides the cost-based guide, rates also reflect policy decisions, implementation strategy, and community values.
The COS determines what each class should pay based on cost causation. But policy decisions — around equity, low-income support, conservation, economic development, and rate stability — may mean rates don't perfectly match COS. Industry practice is to move toward COS over time, avoiding rate shock while reducing cross-subsidization.
James Bonbright's foundational principles guide rate design decisions. Well-designed rates should satisfy multiple, sometimes competing, objectives:
Rates must generate enough revenue to cover the utility's total revenue requirement, including reserves and debt service.
Costs should be fairly apportioned among customer classes based on cost causation, minimizing subsidies between classes.
Rates should be stable and predictable. Sudden, large increases create "rate shock" and erode customer trust. Multi-year phase-ins are preferred.
Revenue should be relatively insensitive to weather, economic conditions, or usage fluctuations. This argues for stronger fixed cost recovery.
Price signals should encourage efficient use of resources. Time-of-use rates and demand charges promote better utilization of system assets.
Rates must be understandable, easy to administer, and uncontroversial as to interpretation. Complexity reduces customer acceptance.
Most electric rates have up to three components, each recovering different types of costs:
How fixed, demand, and energy charges combine in a customer's monthly bill.
| Component | What It Recovers | How It's Charged | Common For |
|---|---|---|---|
| Customer Charge | Meter, billing, service drop — costs of being connected | Fixed $/month | All customer classes |
| Demand Charge | Infrastructure sized for peak — generation, transmission, distribution capacity | $/kW of peak demand | Commercial, Industrial |
| Energy Charge | Fuel, purchased power, variable O&M | $/kWh consumed | All customer classes |
| Energy Cost Adj. | Fuel price volatility (pass-through) | $/kWh rider | All classes (separate line) |
Next: Modern technology is enabling new rate strategies that weren't possible with traditional meters.
Advanced Metering Infrastructure (AMI) and smart grid technology are expanding the toolkit available for rate design. These modern approaches give utilities more options for aligning rates with actual cost causation.
Different prices for on-peak, off-peak, and shoulder periods. Encourages customers to shift usage away from peak hours, improving system load factor.
Very high rates during 10-20 critical peak hours per year (often $0.50-$1.00+/kWh). Customers are notified in advance and can reduce usage to avoid the premium.
Prices change hourly based on actual wholesale market conditions. Typically aligned with ISO/RTO market signals. Best suited for large, sophisticated customers who can respond to price signals in real time.
Specialized rates for electric vehicle charging, distributed solar, and battery storage that reflect the unique load profiles and grid impacts of these technologies.
Aggregations of distributed resources — rooftop solar, batteries, smart thermostats, EV chargers — coordinated to dispatch as a single resource. By 2026, several utilities operate VPPs at scale, using them for peak shaving, ancillary services, and capacity. Rate design must account for how VPP participants are compensated and how costs are allocated to non-participants.
Issued in 2020, Order 2222 requires RTOs/ISOs to allow distributed energy resource aggregations to participate in wholesale capacity, energy, and ancillary services markets. PJM’s compliance was approved in July 2024, with capacity market implementation effective February 2027 and DER aggregation participation beginning in the 2028/2029 capacity year. This creates new revenue streams for DER owners and new cost allocation questions for retail rate designers.
Hyperscale data centers present unique rate design challenges: extremely high load factors (90%+), large demand (50–500+ MW per facility), willingness to co-locate with generation, and interest in 24/7 clean energy matching. Utilities are developing dedicated large-load tariffs, economic development riders, and infrastructure cost-sharing agreements to serve this rapidly growing customer segment.
AMI provides 15-minute interval data for all customers, creating the foundation for class load profiles, demand-based cost allocation, and time-differentiated rates. This data transforms COS from relying on proxy estimates to using actual measured customer behavior.
Next: All of these technical decisions ultimately serve broader policy goals — the final module connects methodology to strategy.
A rate strategy document serves as the framework and roadmap for the utility's cost of service and rates. It aligns with the utility's overall strategy and provides enduring guidance for staff and the governing board.
How should the utility support vulnerable customers? Through rate discounts, internal programs (round-up), or external assistance programs?
Should rates fully align with COS? If subsidies exist between classes, how quickly should they be eliminated? Multi-year phase-in approaches prevent rate shock.
Should rates incentivize distributed generation, EVs, or energy efficiency? How do time-of-use rates enable customer choice in consumption patterns?
What level of reserves, debt service coverage, and cash on hand does the utility target? How do these metrics influence rate-setting decisions?
Should new customers bear the full cost of infrastructure extensions, or should existing customers share? What role does economic development play in rate decisions?
Best practice: comprehensive rate study every 3-5 years, with annual staff review. Rates should be incrementally adjusted to reach revenue targets without rate shock.
Performance-Based Regulation (PBR): A growing number of states — including Hawaii, Minnesota, and Illinois — are moving beyond traditional cost-of-service regulation toward outcome-based incentive frameworks. PBR ties utility earnings to performance metrics (reliability, customer satisfaction, clean energy deployment) rather than capital investment alone. While most NewGen clients operate under traditional regulation, PBR concepts are increasingly influencing how regulators evaluate rate case filings.
Every rate study tells a story — about where your utility has been, where it’s going, and what it owes the customers who depend on it. We help you tell that story with clarity, confidence, and credibility.
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Our recommendations are built on rigorous methodology that holds up under scrutiny — whether in a city council chamber, before a public utility commission, or in front of a cooperative board. When your rates are challenged, our work speaks for itself.
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The utility landscape is changing fast — distributed energy, electrification, aging infrastructure, shifting demographics. We don’t just solve today’s rate case. We help you build a framework for the challenges ahead.
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