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Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids

Cassia Nunes Almeida, Arun Narayanan, Hafiz Majid Hussain, Pedro H. J. Nardelli

TL;DR

This paper evaluates DSM strategies in microgrid contexts with high renewable penetration by comparing profile steering, a non-pricing control method, against pricing-based signals in a Helsinki neighborhood. Using the DEMKit/ALPG simulation framework, it analyzes peak-load reduction, losses, and device profiles under three steering configurations: 100% control, 100% price, and a 50/50 mix. The findings show that peak-load–oriented steering reduces peaks and losses, while price-based DSM worsens peaks and losses, implying policy and design implications for DSM incentives. The work demonstrates the practical viability of decentralized, profile-driven DSM in aging distribution networks and informs decisions on DSM design for better grid reliability.

Abstract

In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.

Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids

TL;DR

This paper evaluates DSM strategies in microgrid contexts with high renewable penetration by comparing profile steering, a non-pricing control method, against pricing-based signals in a Helsinki neighborhood. Using the DEMKit/ALPG simulation framework, it analyzes peak-load reduction, losses, and device profiles under three steering configurations: 100% control, 100% price, and a 50/50 mix. The findings show that peak-load–oriented steering reduces peaks and losses, while price-based DSM worsens peaks and losses, implying policy and design implications for DSM incentives. The work demonstrates the practical viability of decentralized, profile-driven DSM in aging distribution networks and informs decisions on DSM design for better grid reliability.

Abstract

In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.
Paper Structure (4 sections, 5 figures, 1 table)

This paper contains 4 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Results & Discussions
  4. Conclusions

Figures (5)

  • Figure 1: Power flow results for simulations at the local lower voltage grid of the street with 10 houses at Helsink generated before and after being optimized at DEMKit Hoogsteen2019DEMKit for the 2017 year
  • Figure 2: Power profiles and Losses results for the load flow at the local lower voltage grid of the street with 10 houses at Helsinki generated before and after being optimized at DEMKit Hoogsteen2019DEMKit for 3 days in June of 2017
  • Figure 3: Power profiles and Losses results for the load flow at the local lower voltage grid of the street with 10 houses at Helsinki generated before and after being optimized at DEMKit Hoogsteen2019DEMKit for 3 days in December of 2017
  • Figure 4: Power profiles results for two different set of devices from the street of 10 houses generated before and after being optimized at DEMKit Hoogsteen2019DEMKit for 3 days in June of 2017
  • Figure 5: Power profiles results of devices like: dish washer, washing machine, battery, electric vehicles, and photovoltaic panels from one house of the street of 10 houses in Helsinki generated before and after being optimized at DEMKitHoogsteen2019DEMKit for 3 days in June of 2017