We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator are also proven.
Current reserve procurement approaches ignore the stochastic nature of reserve asset availability itself and thus limit the type and volume of reserve offers. This paper develops a reliability-aware probabilistic approach that allows renewable generators to offer reserve capacity with reliability attributes. Offers with low reliability are priced at lower levels. The original non-convex market clearing problem is approximated by a MILP reformulation. The proposed probabilistic reserve procurement allows restricted reserve providers to enter the market, thereby increases liquidity and has the p…
We propose a scenario-oriented approach for energy-reserve joint procurement and pricing for electricity market. In this model, without the empirical reserve requirements, reserve is procured according to all possible contingencies and load/renewable generation fluctuations with the minimum expected system total cost. The innovative locational marginal pricing approach for loads, generations and reserve and the associated settlement process are proposed. We show that payments from loads, payments to generators and congestion rent will reach their balance in the basecase as well as in all scena…
The growing penetration of variable renewable energy sources (VRES) requires additional flexibility reserve to ensure reliable power system operations. Current industry practice typically assumes a certain fraction of the VRES power production forecast as flexibility reserve, thus ignoring other relevant information, such as weather conditions. To address this, probability- and risk-based reserve sizing models have been proposed, which use probabilistic VRES power forecasts that mostly rely on historical forecast and actual VRES power data for model training. Hence, these approaches are not su…
Current contingency reserve criteria ignore the likelihood of individual contingencies and, thus, their impact on system reliability and risk. This paper develops an iterative approach, inspired by the current security-constrained unit commitment (SCUC) practice, enabling system operators to determine risk-cognizant contingency reserve requirements and their allocation with minimal alterations to the current SCUC practice. The proposed approach uses generator and transmission system reliability models, including failure-to synchronize and adverse conditions, to compute contingency probabilitie…
In [1], a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism. To make the discussion more general, in this paper, the single-period co-optimization model (II) in [1] will be extended into a one-shot multi-period co-optimization model, where the coupling of ramping and reserve in multi-period operations will be considered. Also, the multi-period pricing approach and settlement process associated with the multi-period co-optimization model will be discussed. In addition, in c…
The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility o…
Maintaining the stability of renewable-dominant power systems requires the procurement of virtual inertia services from non-synchronous resources (e.g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources (e.g., thermal generators). However, the pricing of inertia provision has not been studied in a stochastic electricity market, where the uncertainty characteristics of renewable energy sources (RES) are considered. To fill in this research gap, this paper formulates a chance-constrained stochastic unit commitment model with inertia requirements and…
Recently, chance-constrained stochastic electricity market designs have been proposed to address the shortcomings of scenario-based stochastic market designs. In particular, the use of chance-constrained market-clearing avoids trading off in-expectation and per-scenario characteristics and yields unique energy and reserves prices. However, current formulations rely on symmetric control policies based on the aggregated system imbalance, which restricts balancing reserve providers in their energy and reserve commitments. This paper extends existing chance-constrained market-clearing formulations…
Large-scale integration of renewable energy sources (RES) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertainties from RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering the temporal correlation and conditional correlation of wind power forecast is formulated to better capture the uncertainty of wind power. The dimensio…
An effective distribution electricity market (DEM) is required to manage the rapidly growing small-scale distributed energy resources (DERs) in distribution systems (DSs). This paper proposes a day-ahead DEM clearing and pricing mechanism to account for the uncertainty of DERs and the coordination with the wholesale electricity market (WEM) through a bi-level model. The upper-level model clears the WEM in the transmission system (TS) and forms the locational marginal price (LMP) and uncertainty LMP (ULMP) for energy and uncertainty/reserve, respectively. In the lower level, a robust scheduling…
Modern market management systems continue to evolve due to the intentions to improve system security and reliability. This evolvement has been leading to a transition of market auction models from a deterministic structure with approximations on the reliability criteria (e.g., acquirement of contingency reserve through proxy reserve policies) to explicit representation of contingencies (e.g., estimation of post-contingency states via participation factors and stochastic programming). This paper proposes a comprehensive framework to establish various procedures for evaluating: (i) transparency …
The roll-out of stochastic renewable energy sources (RES) undermines the efficiency of power system and market operations. This paper proposes an approach to derive electricity prices that internalize RES stochasticity. We leverage a chance-constrained AC Optimal Power Flow (CC AC-OPF) model, which is robust against RES uncertainty and is also aware of the resulting variability (variance) of the system state variables. Using conic duality theory, we derive and analyze energy and balancing reserve prices that internalize the risk of system limit violations and the variance of system state varia…
By acquiring cloud-like capacities at the edge of a network, edge computing is expected to significantly improve user experience. In this paper, we formulate a hybrid edge-cloud computing system where an edge device with limited local resources can rent more from a cloud node and perform resource allocation to serve its users. The resource procurement and allocation decisions depend not only on the cloud's multiple rental options but also on the edge's local processing cost and capacity. We first propose an offline algorithm whose decisions are made with full information of future demand. Then…
This paper studies the procurement and pricing of inertial response using a frequency-constrained unit commitment formulation, which co-optimizes the provision of energy and inertia services while accounting for their complementary properties. The proposed approach builds on a two-step process that allows to differentiate between the units being online for energy purposes and the ones committed additionally solely for inertia provision. Subsequently, three novel pricing and payment schemes that reimburse inertia providers in a transparent and fair manner according to their individual participa…
With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines. First, we focus on the …
Modern electricity grids throughout the world, particularly in islands such as Great Britain, face a major problem on the road to decarbonisation: the significantly reduced level of system inertia due to integration of Renewable Energy Sources (RES). Given that most RES such as wind and solar are decoupled from the grid through power electronics converters, they do not naturally contribute to system inertia. However, RES could support grid stability through appropriately controlling the converters, but currently no market incentives exist for RES to provide this support. In this paper we devel…
As the proportion of variable inverter-based renewable energy generation in electricity systems increases from a minority to the majority of total supply, the complexity and cost of providing ancillary system services increases in parallel. Australia is experiencing this shift now - from having the third most carbon-intensive electricity sector in the world in 2010, to now having penetrations of variable renewable energy (VRE) regularly reaching 100 percent in some regions, with world-leading uptake of distributed energy resources (DER). This paper presents pioneering work exploring new techni…
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfac…
University of Kansas（堪萨斯大学）；Amazon Web Services（亚马逊云科技）
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties whi…
