AC-feasible Local Flexibility Market with Continuous Trading

This paper proposes a novel continuous Local Flexibility Market where active power flexibility located in the distribution system can be traded. The market design engages the Market Operator, the Distribution System Operator and Market Participants with dispatchable assets. The proposed market operates in a single distribution system and considers network constraints via AC network sensitivities, calculated at an initial network operating point. Trading is possible when AC network constraints are respected and when anticipated network violations are alleviated or resolved. The implementation allows for partial bid matching and is computationally light, therefore, suitable for continuous trading applications. The proposed design is thoroughly described and is demonstrated in a test distribution system. It is shown that active power trading in the proposed market design can lead to resolution of line overloads.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed

Qualification and quantification of reserves in power systems under high wind generation penetration considering demand response

Erdinç, Ozan (Arel Author)The presence of high levels of renewable energy resources (RES) and especially wind power production poses technical and economic challenges to system operators, which under this fact have to procure more ancillary services (AS) through various balancing mechanisms, in order to maintain the generationconsumption balance and to guarantee the security of the grid. Traditionally, these critical services had been procured only from the generation side, yet the current perception has begun to recognize the demand side as an important asset that can improve the reliability of a power system, offering notable advantages. In this study, a two-stage stochasti programming model, representing the day-ahead market clearing procedure on an hourly basis and the actual minute-to-minute operation of the power system, is developed comprising different services that specifically address various disturbance sources of the normal operation of a power system, namely intra-hour load variation, intra-hour wind variation, as well as generating unit and transmission line outages. Index Terms—Ancillary services, contingency reserves, demand response, load-following reserves, stochastic programming

Load-Following Reserves Procurement Considering Flexible Demand-Side Resources Under High Wind Power Penetration

Erdinç, Ozan (Arel Author)The variable and uncertain nature of the leading renewable energy resources, such as wind power generation, imposes the development of a sophisticated balance mechanism between supply and demand to maintain the consistency of a power system. In this study, a two stage stochastic programming model is proposed to procure the required load-following reserves from both generation and demand side resources under high wind power penetration. Besides, a novel load model is introduced to procure flexible reserves from industrial clients. Load following reserves from load serving entities (LSE) are also taken into account as well as network constraints, load shedding and wind spillage. The proposed methodology is applied to an illustrative test system, as well as to a 24-node system

Optimum generation scheduling based dynamic price making for demand response in a smart power grid

Smart grid is a recently growing area of research including optimum and reliable operation of bulk power grid from production to end-user premises. Demand side activities like demand response (DR) for enabling consumer participation are also vital points for a smarter operation of the electric power grid. For DR activities in end-user level regulated by energy management systems, a dynamic price variation determined by optimum operating strategies should be provided aiming to shift peak demand periods to off-peak periods of energy usage. In this regard, an optimum generation scheduling based price making strategy is evaluated in this paper together with the analysis of the impacts of dynamic pricing on demand patterns with case studies. Thus, the importance of considering DR based demand pattern changes on price making strategy is presented for day-ahead energy market structure

Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

\u3cp\u3eIn this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.\u3c/p\u3

A multi-objective optimization approach to risk-constrained energy and reserve procurement using demand response

\u3cp\u3eThe large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method.\u3c/p\u3

Multi-objective optimization of radial distribution networks using an effective implementation of the ε-constraint method

Distribution Systems (DS) are usually structured as weakly-meshed but the majority of them operate with a radial topology, mainly in order to accommodate the protection coordination. Obtaining the optimal radial configuration under several criteria has been an active research topic for more than two decades. Because of the computational burden and the non-linearity of the problem, the majority of the proposed methods and techniques, single or multi-objective, use various meta-heuristics. The DS reconfiguration problem, respecting the radiality constraints, is formulated in this paper as a multi- objective Mixed-Integer Linear Programming (MILP) problem. An adequate representation of the Pareto set is produced using an improved implementation of the ε-constrained method. The objective is to determine the optimal radial configuration during several time intervals, minimizing the active power losses and the cost emerging from the switching operations. The proposed methodology is tested using a 16-node sample system

Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households

\u3cp\u3eThe recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.\u3c/p\u3

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

Erdinç, Ozan (Arel Author)As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies