Abstract
Due to increasing numbers of intermittent and distributed generators in power systems, there is an increasing need for demand responses to maintain the balance between electricity generation and use at all times. For example, the electrification of transportation significantly adds to the amount of flexible electricity demand. Several methods have been developed to schedule such flexible energy consumption. However, an objective way of comparing these methods is lacking, especially when decisions are made based on incomplete information which is repeatedly updated. This paper presents a new benchmarking framework designed to bridge this gap. Surveys that classify flexibility planning algorithms were an input to define this benchmarking standard. The benchmarking framework can be used for different objectives and under diverse conditions faced by electricity production stakeholders interested in flexibility scheduling algorithms. Our contribution was implemented in a software toolbox providing a simulation environment that captures the evolution of look-ahead information, which enables comparing online planning and scheduling algorithms. This toolbox includes seven planning algorithms. This paper includes two case studies measuring the performances of these algorithms under uncertain market conditions. These case studies illustrate the importance of online decision making, the influence of data quality on the performance of the algorithms, the benefit of using robust and stochastic programming approaches, and the necessity of trustworthy benchmarking.
Highlights
The integration of renewable energy is central for achieving energy security in a zero-carbon energy future [1]
Over 70 publications of demand-side management were reviewed in [3] to establish a general framework for such approaches; the authors analyzed whether users made selfish or cooperative decisions; the problem is solved with deterministic or stochastic methods, and the algorithms are offline versus online
Transmission system operators (TSOs) and distribution system operators (DSOs) around the world have identified the benefits of using demand response (DR) programs to prevent congestion or ensuring balance of supply and demand
Summary
The integration of renewable energy is central for achieving energy security in a zero-carbon energy future [1]. Maximizing their use requires different operation and planning strategies to those traditionally used for controllable generators. Over 70 publications of demand-side management were reviewed in [3] to establish a general framework for such approaches; the authors analyzed whether users made selfish or cooperative decisions; the problem is solved with deterministic or stochastic methods, and the algorithms are offline versus online. In [7], a new approach that uses hierarchical control was compared to algorithms assuming central control, which are known for finding system-wide optimal solutions. The challenge of assessing strategies increases when stakeholders consider real-time decisions, which require online algorithms that update decisions based on new information. Simulations can help with developing better online optimization algorithms for complex dynamic problems [9]
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