Modelling dynamic demand response for plug-in hybrid electric vehicles based on real-time charging pricing

© The Institution of Engineering and Technology. Based on the benefits of real-time pricing both to individual users and the society as a whole, this study introduces a real-time charging price (RTCP) mechanism supported by an intelligent charging management module into plug-in hybrid electric vehicles (PHEVs) charging environment. The optimal RTCP is executed by a distributed algorithm using a utility model to maximise the whole charging system welfare. The willingness-to-charge parameter is derived to reflect the charging preferences of PHEV users and their different responses to the RTCP. Several scenarios are established to discuss the effect of both the RTCP and willingness-to-charge on charging load. The simulation results show that reasonable charging will be realised based on the optimal RTCP mechanism

Distributed smart charging of electric vehicles for balancing wind energy

To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm

A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized $\alpha$-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.Comment: 6 pages, 4 figure

Real-Time Pricing Strategy Based on the Stability of Smart Grid for Green Internet of Things

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy

Load shifting of a supplier-based demand response of multi-class subscribers in smart grid

We propose a demand response (DR) solution approach for a real-time pricing model with multi-class users to determine the electricity supply mix. The model aims to address the problem of power consumption overloading in peak hours using the real-time information obtained from the interaction between suppliers and users in a smart grid. The proposed DR algorithm allocates the overloaded demand assigned to a supplier to other electricity suppliers in order to satisfy all users’ demand while the supplier ensures to maximize the utility or reserved demand of users. Furthermore, a priority approach based on different user groups is developed for allocating the extra demand to other suppliers. Numerical experiments have been conducted to analyze the performance of the algorithm and compare the real-time electricity price with the fixed price

Optimal Scheduling of Critical Peak Pricing for a Power Retailer Based on the Multi-Stage Stochastic Analysis Under Day-ahead Imbalance Band Market

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 윤용태.본 논문에서는 전력 시스템 내의 신재생에너지원 출력, 도매 pool 시장의 전력가격, 전력 부하 및 경쟁사의 전략 등의 무작위 요인으로부터 비롯되는 불확실성에 대응하기 위해 다중단계 추계학적 방법론을 적용한 판매사업자의 최적 피크 요금제 스케줄링 방안을 제안한다. 판매사업자가 기존의 결정론적 및 단일단계 추계학적 방법론을 사용하여 피크 요금제를 스케줄링 할 경우, 의사 결정 시점에서 전체 기간의 최적값이 도출되므로 불확실성 요소의 변화에 대응하기 어렵다. 반면, 다중단계 추계학적 방법론을 적용할 경우에는 각 단계에서 갱신되는 정보를 바탕으로 순차적으로 의사 결정을 내릴 수 있다. 다중단계 추계학적 방법론에서 불확실성 요소의 추계학적 과정은 시나리오 트리 구조를 통해 반영하였으며, 비선형성으로 인한 계산의 복잡도를 줄이기 위해 big-M 방법과 같은 선형 근사 기법을 구현하여 목적함수를 혼합 정수 선형 계획법 (mixed-integer linear programming, MILP) 문제로 변환하였다. 대상 전력 시장으로는 Power Exchange for Frequency Control (PXFC) 시장 환경을 도입하였다. 기존 도매시장 구조에서 수급균형 비용은 비용 사회화 원칙에 기반하여 모든 시장 참여자에게 할당된다. 이 경우 전체 불균형량에 대한 개별 시장 참여자의 기여는 고려되지 않으므로, 시장 참여자의 불균형 유발을 방지하기 위한 적절한 인센티브 또는 패널티가 존재하지 않는다. 한편, PXFC 시장 구조에서는 수급균형 비용이 비용 유발자 원칙에 따라 할당되므로 이에 따라 판매사업자는 각자가 발생시킨 불균형량에 대한 공정하고 투명한 가격 신호를 받을 수 있으며 불균형량을 줄이기 위해 기존 도매시장보다 더 적극적인 역할을 수행하게 된다. 따라서 본 논문에서는 판매사업자의 불확실성이 증가하는 환경에서 이에 따른 수급균형 비용을 고려한 판매사업자의 전략을 정량적으로 분석하기 위해 PXFC 시장 환경을 상정하였다. 단, 시장 구조 설계 시에는 특정 시장 참여자 입장에의 고려가 아닌, 전체적인 시장 참여자들의 행동을 예측 및 해석하고 이를 반영하는 사회 후생 극대화 측면의 거시적인 관점이 필요하므로 본 학위논문에서는 PXFC 시장 구조 설계에의 연구는 포함하지 않았음을 밝힌다. 사례연구에서는 다중단계 추계학적 방법론을 적용한 피크 요금제 운영의 효과를 검증하기 위해, 자체 생산시설로 태양광 발전원을 소유하며 최종 소비자에게 피크 요금제를 제공하는 판매사업자의 기대 수익을 다양한 케이스에서 비교하였다. 케이스는 단일단계 추계학적 방법론과 다중단계 방법론으로 나뉘며, 다중단계 방법론에서는 단계 세분성 및 결정론적 접근법과의 결합 여부에 따라 다르게 구성되었다. 모의 결과 단일단계 추계학적 방법론에 비해 다중단계 추계학적 방법론에서 판매사업자의 기대수익이 증가하였으며, 다중단계 방법론에서는 단계의 세분성이 높아질 수록 피크 이벤트의 발령 시점이 분산되어 위반량 및 패널티 비용이 감소하고 기대수익이 증가함을 확인하였다. 다음으로 결정론적 방법론을 결합한 하이브리드 (hybrid) 케이스에서는 위반량의 최댓값을 제한하여 재무적인 위험이 높은 경우에서도 일정 수익을 보장할 수 있음을 보였다. 마지막으로 민감도 분석을 통해 세 가지 주요 매개변수가 판매사업자의 수익 변동에 미치는 영향을 분석하였다. 본 연구는 자유화된 전력 시장 구조에서 판매사업자가 고려해야 하는 불확실성 요소가 증가할 경우, 판매사업자의 의사 결정을 위한 효율적이고 안전한 방법론으로 활용할 수 있을 것이다.In this paper, a novel multi-stage stochastic programming (MSSP) model is proposed for a power retailer to schedule critical peak pricing (CPP) considering uncertainties in demand and the generation of photovoltaics. When using deterministic or single-stage stochastic methodology, retailers face difficulties in coping with changes in uncertainty because the optimal values of the entire period are determined once at the time of decision-making. On the other hand, with the multi-stage stochastic model, retailers can revise their decision at each stage recursively based on information updated gradually over time. In the multi-stage stochastic model, the stochastic process of the random variables can be approximated in the form of a scenario tree structure. In addition, to reduce the computational complexity due to non-linearity, linear techniques, such as the big-M method, are implemented to transform the objective function into a mixed-integer linear programming problem. For the target market, we adopt a power exchange for frequency control (PXFC) market. Under the typical wholesale market structure, balancing costs are allocated to all market participants in a socialized manner. Within this structure, it is difficult to give appropriate incentives or penalties to market participants, because the contribution of individual market participants to the imbalance is not considered. Conversely, in the PXFC market structure, balancing costs are allocated based on a cost-causality principle. Retailers can thereby receive fair and transparent price signals and play a more active role to reduce the imbalances they cause in the PXFC market compared to the typical wholesale market. In summary, this paper introduces the PXFC market to analyze retailer strategy considering its balancing costs. However, in designing the market structure, it is necessary to take a macroscopic view in terms of maximizing social welfare by reflecting the prediction and interpretation of entire market participants behavior, rather than considering specific market participants' position. Therefore, it is noted that research on the PXFC market structure design is not included in this thesis. The effectiveness of the proposed method in terms of expected profit is analyzed using numerical simulations compared with several case studies. Case studies are divided according to methodology, stage granularity, and whether they are combined with a deterministic approach. The results show that the expected value of profit increased in the multi-stage stochastic model compared with the single-stage stochastic model. Also, the case with finer stage granularity yielded higher expected profit. This is due to the reduction in penalty costs following the distribution of critical events to the stage in which the imbalance violates the reserve band. A hybrid case combined with a deterministic approach could guarantee a profit by restricting the maximum number of violations in high-risk scenarios. Finally, through sensitivity analysis, we analyze the effect of the three main parameters which are critical peak rate, penalty price and minimum interval between successive critical events on the profit change of the retailer. This study can be used as an analysis of changes in retailer strategy in the liberalized power market structure and also as an efficient and safe methodology for retailer decision-making under an environment of increasing uncertainties in the power system.제 １ 장 서 론 1 제 １ 절 연구의 배경 및 목적 1 제 ２ 절 논문의 구성 8 제 ２ 장 시장 기반 수급균형 비용 분배 원칙 10 제 １ 절 기존 도매시장: 비용 사회화 원칙 10 제 ２ 절 POWER EXCHANGE FOR FREQUENCY CONTROL 시장: 비용 유발자 부담 원칙 15 제 ３ 절 판매사업자에의 수급균형 비용 할당 17 제 ３ 장 판매사업자의 전략 비교: 기존 도매시장 및 PXFC 시장 20 제 １ 절 피크 요금제 20 제 ２ 절 피크 요금제 운영 전략 정식화 23 제 ４ 장 판매사업자의 불확실성과 다중단계 추계학 모델 34 제 １ 절 추계학 모델의 수학적 및 기술적 구조 34 제 ２ 절 다중단계 추계학적 프로그래밍 37 제 ３ 절 시나리오 트리 생성 및 시나리오 저감 방안 39 제 ４ 절 판매사업자의 추계학적 피크 요금제 운영 전략 정식화 47 제 ５ 장 사례연구 57 제 １ 절 시뮬레이션 환경 57 제 ２ 절 시뮬레이션 결과 및 분석 64 제 ６ 장 결론 82Docto

Optimal Distribution Reconfiguration and Demand Management within Practical Operational Constraints

This dissertation focuses on specific aspects of the technical design and operation of a `smart\u27 distribution system incorporating new technology in the design process. The main purpose of this dissertation is to propose new algorithms in order to achieve a more reliable and economic distribution system. First, a general approach based on Mixed Integer Programming (MIP) is proposed to formulate the reconfiguration problem for a radial/weakly meshed distribution network or restoration following a fault. Two objectives considered in this study are to minimize the active power loss, and to minimize the number of switching operations with respect to operational constraints, such as power balance, line ow limits, voltage limit, and radiality of the network. The latter is the most challenging issue in solving the problem by MIP. A novel approach based on Depth-First Search (DFS) algorithm is implemented to avoid cycles and loops in the system. Due to insufficient measurements and high penetration of controllable loads and renewable resources, reconfiguration with deterministic optimization may not lead to an optimal/feasible result. Therefore, two different methods are proposed to solve the reconfiguration problem in presence of load uncertainty. Second, a new pricing algorithm for residential load participation in demand response program is proposed. The objective is to reduce the cost to the utility company while mitigating the impact on customer satisfaction. This is an iterative approach in which residents and energy supplier exchange information on consumption and price. The prices as well as appliance schedule for the residential customers will be achieved at the point of convergence. As an important contribution of this work, distribution network constraints such as voltage limits, equipment capacity limits, and phase balance constraints are considered in the pricing algorithm. Similar to the locational marginal price (LMP) at the transmission level, different prices for distribution nodes will be obtained. Primary consideration in the proposed approach, and frequently ignored in the literature, is to avoid overly sophisticated decision-making at the customer level. Most customers will have limited capacity or need for elaborate scheduling where actual energy cost savings will be modest

Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand