Spinning Reserve Services Research Articles

A decision-making model for joint energy and reserve scheduling of wind power producers with local intraday demand response exchange market

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

A general model of optimal energy storage operation in the market conditions

In liberalized electricity markets, energy storage devices, especially those with high capacity, can generate income through multiple services. In this paper, a general model of energy storage operation, suitable for different optimizations and comparisons of various storage technologies in market-oriented power systems, is presented. The objective function maximizes the possible income of storage units (based on the daily operation cycle) earned from energy arbitration and several ancillary services, considering various constraints and market rules for service providers. The mixed integer linear programming (MILP) optimization technique is used to solve the objective function. In numerical examples, the optimal operation modes and possible incomes for typical battery and typical pumped storage hydropower plant (PSHP), using the achieved electricity market prices of energy and the ancillary services (regulation-up, regulation-down and spinning reserve services) in one year, were compared. The influence of the efficiency coefficient, as well as the influence of various PSHP inflows on storage policy, were investigated. The proposed model has proven robust and applicable for various analyses of storage unit operations. The performed numerical calculations have shown that with the decreasing of storage device efficiency coefficient, the incomes generated through ancillary services become more significant.

Harnessing Ramp Capability of Spinning Reserve Services for Enhanced Power Grid Flexibility

Flexible ramping product (FRP) has been recently launched in a number of electricity markets to enhance the power grid flexibility and accommodate additional deals of uncertainty introduced by renewable energy resources. The FRP procures additional ramp capacities provided by the dispatchable generating units and consumers in the real-time market (RTM) with the aim to enhance the ability of supply to closely follow the net-load at the next immediate time-interval. An important challenge in widespread adoption of this technology is a key question: Does the available energy capacity of the RTM participants contain sufficient ramp capacity to tackle the net-load uncertainties? Indeed, shortage of the systems ramp capacity may not only jeopardize its flexibility, but also may cause drastic fluctuations in the RTM energy prices. To address this challenge, this paper proposes a novel optimization framework to harness the procured spinning reserve services in the day-ahead market (DAM) for FRP provision in the RTM while meeting the system reliability requirements. On this basis, the FRP will be simultaneously procured from the RTM energy and DAM spinning reserve capacities. Furthermore, a new payment mechanism is proposed to compensate the portion of spinning reserve utilized for FRP procurement in the RTM. The proposed framework is simulated on the IEEE 118-bus test system and the results verify its effectiveness in modern electricity markets.

Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations.

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi‐objective model

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides Microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimization (MOO) and decision analysis is put forward to address this model. First, a powerful heuristic optimization algorithm, called the {\theta}-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. And thereby, the best compromise solutions (BCSs) are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

A Techno-economic Analysis on Spinning Reserve Services of Battery Energy Storage Systems for Thermal

A Techno-economic Analysis on Spinning Reserve Services of Battery Energy Storage Systems for Thermal

Modeling and control of water booster pressure systems as flexible loads for demand response

Water Booster Pressure Systems (WBPSs) are responsible for supplying water and maintaining pressure in a building pipeline. These systems are potentially useful to offer energetic services required by the System Operator (SO) through demand response, considering the spread use of these hydraulic devices in high-rise buildings. In this article, a dynamic model for a WBPS is developed in order to evaluate it as a flexible load for demand response applications. The model is built from first-principles and tuned with experimental data of air pressure, power consumption and water flow, obtaining an error of 1.11% in the energy demand between the experimental and the simulated data. It is shown that the WBPS can operate as a flexible load by changing the pressure set point. Additionally, it is achieved a flexibility of 27% in the energy power consumption without stopping the water flow in the building and it is shown that WBPS can provide Spinning Reserve Services. Finally, this work proposes an aggregator of systems, based on a Proportional-Integral Gain Scheduling controller, that can track the SO requirements with an error lower than 0.86%.

Risk Assessment of Virtual Power Plants Offering in Energy and Reserve Markets

Distributed energy resources (DERs) can be integrated into a single entity, namely, virtual power plant (VPP). The integration enables them to participate in competitive wholesale electricity markets. According to the different types of uncertainty faced by different DERs, this integration can also act as a risk-hedging mechanism providing a surplus profit. In this paper, using a two-stage stochastic programming approach, risk-averse optimal offering model for a VPP trading in a joint market of energy and spinning reserve service is presented. The conditional value-at-risk (CVaR) is used to control the risk of profit variability. Uncertainties involved in generation of renewables, consumption of loads, calls for reserve service, as well as prices in the day-ahead market, the spinning reserve market (RM) and the balancing (real-time) market (BM), are taken into consideration. This paper assesses how total and surplus profits of VPP are affected by risk-aversion, participation in the RM, and the pricing system in the BM. For this purpose, the role of the integration and the trends of energy and reserve transactions under both single and dual pricing systems in the BM are addressed in detail through a numerical study.

The Stochastic Bidding Strategy of Micro grid for Participation in Energy and Ancillary Services

Typically, micro-grid is considered as an effective way to integration of distributed generations. However, in the deregulated environment integration of micro-grid in power system should be further considered. In this paper, a stochastic bidding strategy of joint market for the energy and spinning reserve services markets, with taking into account of the uncertainties of renewable distributed generation output power, load and electricity market prices to maximize the profits of micro-grid, is recommended. The bidding strategy is modeled as an optimization problem and is divided into two stages. First the predictions for uncertainties was carried out by using Adaptive Neural Fuzzy Inference System(ANFIS) and according to the forecast errors and Latin Hyperbolic Sampling (LHS) method, scenarios for micro-grid produced and these scenarios is reduced by using backward scenario reducing. In the second stage, according to the generated scenarios in the previous stage, the profit of micro-grid bidding including the revenue of micro-grid bidding, operation and imbalance costs is optimized by genetic algorithm. In the end, micro-grid bids is sent to market. The effectiveness of the proposed method is discussed on a sample micro-grid and results show the effectiveness of the proposed method.

Value of flexible electric vehicles in providing spinning reserve services

As the share of integrated renewable energy sources (RES) increases, traditional operation principles of the power systems need to change in order to maintain reliable and secure service provision, on one hand, and minimal cost and environmentally friendly electricity generation on the other. The challenge of alleviating additional uncertainty and variability brought by new sources to the system operation is seen as defining both flexibility capacities and flexibility requirements through provision of multiple services. In this context the role of emerging technologies, such as electric vehicles (EV) and energy storage (ES), is recognized through their active participation in providing both energy and reserve service.This paper elaborates on the benefits of active EV participation in multiple system services through various charging strategies. The presented mixed integer linear programming (MILP) unit commitment problem (UC) considers the capability of EV to provide primary, secondary and tertiary reserve as well as energy, however the focus is put on the benefits of EV providing spinning reserve services. The results clearly show benefits of multiple EV role to that of providing energy only. In addition the paper analyses multiple power systems, with regards to their energy mix, and recognizes how integration of EVs reflects on power system flexibility through metrics expressed as operational cost, environmental benefits and reduced wind curtailment.

Bidding strategy of microgrid with consideration of uncertainty for participating in power market

Microgrid is commonly regarded as an efficient way for integration of distributed generation (DG) in low voltage network. However, the integration method of microgrid in power system for maximum benefit needs to be further promoted. In this paper, a stochastic bidding strategy of microgrid in a joint day-ahead market of energy and spinning reserve service is proposed taking into account of uncertainty of renewable DG units’ output power and load. The stochastic bidding strategy is modeled as bi-level optimization problem and can be divided into two steps. First, Latin Hypercube Sampling (LHS) is utilized for generating microgrid uncertain net power scenarios according to day-ahead uncertain power scenario models, and then reduced by backward scenario reduction technique for less computation. Second, the upper level total bidding profit including bidding revenue, expected imbalance and operation cost is optimized by interior point algorithm in MATLAB for making optimal bids. The expected imbalance and operation cost is calculated by iteratively invoking lower level deterministic unit commitment under each microgrid uncertain net power scenario. The lower level deterministic unit commitment is coded and solved by mixed integer nonlinear programming (MINLP) solver DICOPT in GAMS. Finally, the optimal energy and spinning reserve bids are given by solving the bi-level bidding model. The model is applied to a modified typical low-voltage microgrid and the effectiveness and excellence of proposed strategy is proven by comparing simulation results with traditional deterministic bidding strategy.

The operating schedule for battery energy storage companies in electricity market

This paper presents a series of operating schedules for Battery Energy Storage Companies (BESC) to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration. As individual market participants, BESC can bid in ancillary services markets in an Independent System Operator (ISO) and contribute towards frequency and voltage support in the grid. Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible. Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems (ESS) required for meeting spinning reserve requirements as well as peak shaving. Historic spot market prices and frequency deviations from Australia Energy Market Operator (AEMO) are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia’s National Electricity Markets (NEM).

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part II: Numerical Analysis

This paper is to evaluate the presented model in part I for bidding strategy of virtual power plant (VPP) with centralized control in a joint market of energy and spinning reserve service. Two test VPPs are introduced and different scenarios are considered for markets prices. At first, the participation of VPP in only energy market is studied. Then, the spinning reserve market is taken into consideration and the bids of VPP in a joint market of energy and spinning reserve service is studied under different scenarios of markets prices and the results are analyzed. In all cases, the results show the effectiveness and the quality of the procedure and validate the proposed model.

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation

This paper addresses the bidding problem faced by a virtual power plant (VPP) in a joint market of energy and spinning reserve service. The proposed bidding strategy is a non-equilibrium model based on the deterministic price-based unit commitment (PBUC) which takes the supply-demand balancing constraint and security constraints of VPP itself into account. The presented model creates a single operating profile from a composite of the parameters characterizing each distributed energy resources (DER), which is a component of VPP, and incorporates network constraints into its description of the capabilities of the portfolio. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and solved by genetic algorithm (GA).

Gaming Analysis in Joint Energy and Spinning Reserve Markets

This study concentrates on the strategic interaction among suppliers in a centralized market where electrical energy and spinning reserve are simultaneously traded. A bilevel optimization technique and a mathematical program with equilibrium constraints approach are utilized to develop optimal bidding strategy for competitive suppliers participating in these markets. The strategic behavior of each supplier is represented via a parameterized Supply Function Equilibria (SFE) model. Two versions of the parameterized SFE are considered. They are referred to as c-parameterization model (involving manipulation of the sole intercept) and (s,c)-parameterization model (involving arbitrarily manipulation of the slope and the intercept). A pay-as-bid pricing mechanism is assumed for settling the market and calculating the supplier profit from selling energy and spinning reserve services. A six-bus test system together with a number of case studies are used to illustrate the interaction between the energy and spinning reserve markets. Transmission limits and the supplier physical constraints are observed in this study. The extension of the method to include a multiperiod market with intertemporal constraints is discussed through numerical results.

Genetic algorithm-based spinning reserve dispatching in a competitive electricity market

Power systems rely greatly on ancillary services in maintaining operation security. As one of the most important ancillary services, spinning reserve must be provided effectively in the deregulated market environment. This paper focuses on the design of an integrated market for both electricity and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the ISO's total payment while ensuring system security. Genetic algorithms are used in the finding of the global optimal solutions for this dispatching problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed market.