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Application of Microgrids in Supporting Distribution Grid Flexibility
Alireza Majzoobi, Amin Khodaei
TL;DR
Renewable growth increases net-load variability and challenges supply-demand balance. The paper schedules microgrid resources around distribution-grid flexibility requirements; simulations report reduced ramps to the operator’s target, with an operating-cost trade-off.
Problem
Growing renewable generation adds net-load variability and makes balancing electricity supply and demand more challenging.
Source: §I · p. 2Method
The paper proposes flexibility-oriented microgrid scheduling that coordinates microgrid net load with aggregated consumer and prosumer load at intra-hour and inter-hour resolutions.
Source: §II · p. 3, §VI · p. 9Results
In Case 2, distribution-net-load variability was reduced to the grid operator’s target of 0.5 MW/10-min; achieving the desired ramping increased microgrid operating cost by $328.7.
Source: §IV · p. 7Takeaways & Limitations
The studied model supports utility-grid ramping requirements at both intra-hour and inter-hour resolutions while retaining the microgrid’s islanding capability.
Source: §V · p. 8, §VI · p. 9
Abstract
from arXiv · showhide
Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply-demand balance. In this paper, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues. The proposed coordination is performed to capture both inter-hour and intra-hour net load variabilities. Numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model.
I. INTRODUCTION
Renewable growth increases net-load variability and makes supply-demand balancing more difficult. The paper proposes using microgrid flexibility to support distribution-grid operations.
I. INTRODUCTION
- Renewable growth can produce abrupt net-load ramps, including changes as large as 13 GW in three hours in California’s projected 33% renewable-target context.The cited example is the duck curve, where renewable generation falls while demand rises.Source: §I · p. 2
- Bulk fast-ramping generation is constrained by limited capacity, high capital costs, construction time, and possible transmission congestion.Related small-scale approaches include demand response, storage, and aggregated electric vehicles, each with deployment obstacles.Source: §I · p. 2
- Microgrids combine local loads and distributed resources as a controllable entity that can operate grid-connected or islanded.Grid-connected operation can import or export power for least-cost scheduling; islanding can isolate the microgrid from upstream faults or disturbances.Source: §I · p. 2
- Unlike market-based flexibility procurement, this paper studies how a microgrid controller can manage local resources to provide grid services, including where a market mechanism is unavailable.The proposed model coordinates microgrid net load with distribution net-load variability limits and considers grid-connected and islanded operation.Source: §I · p. 3
II. MODEL OUTLINE
The model coordinates one microgrid with consumers and prosumers on a distribution feeder. It uses grid-flexibility limits across intra-hour and inter-hour intervals to shape the power exchanged with the utility.
II. MODEL OUTLINE
- The feeder model includes consumers and prosumers alongside one microgrid, with inter-hour and intra-hour periods represented separately.The schematic distinguishes the two time resolutions used in the model.Source: §II · p. 3, Figure 2 · p. 3
- Utility-grid power supplies the feeder’s aggregated net load, and its intra-hour and inter-hour variability is constrained.The equations express limits on changes in utility-grid power across both interval types.Source: §II · p. 3
- Grid operators select flexibility limits using day-ahead net-load forecasts and desired flexibility for each interval.The paper leaves system-level limit determination and cost-benefit analysis for future research.Source: §II · p. 3
- Price-based scheduling may exacerbate feeder variability, whereas flexibility-oriented scheduling coordinates microgrid resources with grid requirements while minimizing operating cost.The figure contrasts increasing variability in its top depiction with capturing variability in its bottom depiction.Source: §II · p. 3, Figure 3 · p. 4
III. FLEXIBILITY-ORIENTED MICROGRID SCHEDULING – PROBLEM FORMULATION
The formulation schedules microgrid resources at least cost while maintaining load balance and respecting operating, exchange, islanding, and flexibility constraints.
III. FLEXIBILITY-ORIENTED MICROGRID SCHEDULING – PROBLEM FORMULATION
- The objective minimizes daily microgrid operating cost, including local generation, utility-grid energy transfers, and outage cost.Outage cost is defined as load curtailment multiplied by the value of lost load.Source: §III · p. 4
- The load-balance equation matches microgrid demand with local DER output, storage, and utility-grid power exchange.Load curtailment allows a feasible solution when islanded operation lacks adequate generation.Source: §III · p. 4
- Utility-grid exchange can represent import, export, or zero transfer when connected, and is restricted by exchange and flexibility constraints.The binary islanding parameter allows interaction with the utility grid only during grid-connected operation.Source: §III · p. 4
A. Operation Constraints (Os)
The operation constraints model dispatchable generation, storage, and adjustable loads, while accounting for their limits and time-dependent operating requirements.
A. Operation Constraints (Os)
- Only dispatchable generation, energy storage, and adjustable loads provide flexibility because they are controllable.Loads are classified as fixed or adjustable, and generation units as dispatchable or nondispatchable.Source: A. Operation Constraints (Os) · p. 4
- Dispatchable units are constrained by minimum and maximum output, ramp-up and ramp-down rates, and minimum up and down times.The formulation applies ramp limits at both intra-hour and inter-hour resolutions.Source: A. Operation Constraints (Os) · pp. 4–5
- Storage constraints model charging or discharging modes, power limits, stored-energy capacity, and minimum charging and discharging times.The formulation prevents simultaneous charging and discharging and updates stored energy across both interval types.Source: A. Operation Constraints (Os) · pp. 4–5
- Adjustable loads have minimum and maximum power limits, minimum operating times, and required energy for their operating cycle.Their binary operating state indicates whether a load is consuming power.Source: A. Operation Constraints (Os) · pp. 4–5
B. Flexibility Constraints (Fs)
The flexibility constraints limit microgrid power exchange to align its net load with the aggregated load of connected consumers and prosumers. Time-dependent microgrid limits translate grid-operator flexibility requirements into net-load limits, with communication routed through the operator.
B. Flexibility Constraints (Fs)
- The constraints bound microgrid power exchange so its net load matches the aggregated connected prosumer and consumer net load, capturing likely variations.Source: B. Flexibility Constraints (Fs) · p. 5
- The formulation substitutes the microgrid net-load expression into existing constraints to derive inter-hour and intra-hour flexibility bounds.Source: B. Flexibility Constraints (Fs) · p. 5
- Grid-operator flexibility limits are constant, while microgrid net-load limits vary with aggregated feeder demand and forecast resolution from 1 minute to 1 hour.Setting Δ1 to zero eliminates intra-hour variations.Source: B. Flexibility Constraints (Fs) · p. 5
- Connected consumers and prosumers are forecasted parameters, and the microgrid communicates with them only through the grid operator.Source: B. Flexibility Constraints (Fs) · p. 5
C. Islanding Considerations
The model represents islanding through a Θ-k criterion that requires operation in islanded mode for k consecutive intra-hour periods. It defines scenarios across the scheduling horizon to account for unknown disturbance timing and duration.
C. Islanding Considerations
- Islanding is intended to disconnect the microgrid rapidly during distribution-network faults, safeguard components from upstream disturbances, and protect voltage-sensitive loads.Source: C. Islanding Considerations · p. 5
- Under the Θ-k criterion, the microgrid is islanded for k consecutive intra-hour periods in each scenario and grid-connected during the other periods.Θ is the total number of intra-hour periods in the scheduling horizon.Source: C. Islanding Considerations · p. 5
- For 10-minute periods, the paper defines 144 scenarios; Fig. 4 shows the first five for Θ-4, and the model applies to any microgrid size.Source: C. Islanding Considerations · p. 5, Figure 4 · p. 6
IV. NUMERICAL SIMULATIONS
The simulations test whether microgrid scheduling can manage feeder net-load ramps while maintaining islanding readiness. Flexibility-oriented scheduling reduces feeder variability to the targeted limits, but tighter limits raise microgrid operating costs.
Simulation setup
- The test feeder combines one microgrid with aggregated prosumer generation and consumption; its net load reaches a 3.3 MW/10-min maximum ramp and 12.9 MW peak.The simulated microgrid includes four dispatchable units, two nondispatchable units, storage, and five adjustable loads.Source: §IV · p. 6, Figure 5 · p. 6
Case 1
- Price-based Case 1 scheduling produces a 21.58 MW utility-grid peak and an 8.9 MW/10-min feeder ramp, worsening net-load variability.The ramp occurs between hours 11 and 12; the peak occurs between hours 9 and 10.Source: §IV · p. 6, Figure 6 · p. 6
Case 2
- In Case 2, flexibility-oriented scheduling uses 0.5 MW/10-min inter- and intra-hour ramp limits and a Θ-1 criterion for any 10-min islanding; unit 4, storage, and all DER dispatch change, costing $12077.Source: §IV · p. 7
- Case 2 reduces feeder net-load variability to the targeted 0.5 MW/10-min ramp limits through flexibility-oriented microgrid scheduling.The utility-grid ramps are confined between the desired values.Source: §IV · p. 7, Figure 7 · p. 7, Figure 8 · p. 7
Case 3
- With zero intra-hour ramping and 2 MW/10-min inter-hour ramping, Case 3 eliminates within-hour feeder net-load variability.Consumption is constant within each operating hour and may change by up to 2 MW between consecutive hours.Source: §IV · pp. 7–8, Figure 10 · p. 8
Ramping-limit sensitivity
- Results across ramping-limit settings are near-optimal, and tighter limits increase microgrid operating cost nonlinearly; load curtailment may occur to preserve flexibility feasibility.The authors identify the need for additional measures to prevent microgrid load curtailment.Source: §IV · p. 8, Table III · p. 8
V. DISCUSSIONS
The proposed microgrid scheduling model combines ramping flexibility constraints with least-cost resource scheduling and islanding reliability. The studied cases present microgrids as localized options for addressing distribution net-load ramping with high-resolution scheduling.
V. DISCUSSIONS
- The model schedules microgrid loads and DERs at least cost while supporting utility-grid net-load ramping and maintaining local-load supply capability during islanding.The Θ-k islanding criterion supports reliability, while inter-hour and intra-hour ramping constraints target different time resolutions.Source: §V · p. 8
- Inter-hour and intra-hour ramping constraints ensure that the utility grid’s desired power is obtained at different time resolutions.Source: §V · p. 8
- The studied cases use 10-minute scheduling to capture net-load variability and allow other intra-hour resolutions.Source: §V · p. 8
- Localized microgrid flexibility can significantly reduce utility investment in generation, transmission, and distribution upgrades, at the small expense of incentivizing flexibility services.Source: §V · p. 8
VI. CONCLUSION
The study proposes flexibility-oriented microgrid scheduling to address distribution net-load variability, especially when solar generation falls as consumer loads rise. Simulations indicate that existing microgrids can help address flexibility challenges while avoiding costly generation and distribution investments.
VI. CONCLUSION
- The targeted net-load variability primarily results from simultaneous decreases in solar generation and increases in consumer loads.Source: §VI · pp. 8–9
- Simulations show that grid operators can leverage existing microgrid flexibility to address distribution-network flexibility challenges while removing the need for costly generation and distribution investments.The simulations considered different desired inter-hour and intra-hour ramping levels.Source: §VI · p. 9
- The proposed model schedules microgrid resources around inter-hour and intra-hour net-load ramping limits during 24-hour day-ahead operation.It also incorporates the Θ-k islanding criterion to maintain the capability to island while supporting the utility grid when grid-connected.Source: §VI · p. 9