Electricity Purchase Research Articles

A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation

Although the locational marginal price may change dramatically within a single day in competitive wholesale electricity markets, most end users are charged monthly electricity bills over flat rates. Without financial incentives, the customers are lacking of motivation to respond to the price signals, which may result in inefficient energy consumption. In Texas, Senate Bill 1125 encourages qualified residential and commercial customer classes to participate in demand response (DR) programs. This paper proposes an idea to aggregate a number of residential customers to participate in residential DR program by employing smart appliances and a home area network to shift the coincidental peak load to off-peak hours to reap financial benefits. The operation strategies for the most representative residential load types are discussed. To further reduce electricity purchase and cut electricity bills, a solar farm with energy storage system is proposed, and the control algorithm is designed accordingly. The operation strategies are simulated for a whole year, and the annual costs are calculated and compared in this paper. The results show that, by doing load control and utilizing renewable resources, the total operation cost can be reduced significantly.

Proton exchange membrane fuel cell for cooperating households: A convenient combined heat and power solution for residential applications

In this paper we compare the technical and economical performances of a high temperature proton exchange membrane fuel cell with those of an internal combustion engine for a 10 kW combined heat and power residential application. In a view of social innovation, this solution will create new partnerships of cooperating families aiming to reduce the energy consumption and costs.The energy system is simulated through a lumped model. We compare, in the Italian context, the total daily operating cost and energy savings of each system with respect to the separate purchase of electricity from the grid and production of the thermal energy through a standard boiler. The analysis is carried out with the energy systems operating with both the standard thermal tracking and an optimized management. The latter is retrieved through an optimization methodology based on the graph theory. We show that the internal combustion engine is much more affected by the choice of the operating strategy with respect to the fuel cell, in terms long term profitability. Then we conduct a net present value analysis with the aim of evidencing the convenience of using a high temperature proton exchange membrane fuel cell for cogeneration in residential applications.

Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation

We propose an agent-based model to simulate how changes to the Italian support scheme will affect the diffusion of PV systems among single- or two-family homes. The adoption decision is assumed to be influenced by (1) the payback period of the investment, (2) its environmental benefit, (3) the household's income, and (4) communication with other agents. The estimation of the payback period considers investment costs, local irradiation levels, governmental support, earnings from using self-produced electricity vs. buying electricity from the grid, administrative fees, and maintenance costs. The environmental benefit is estimated by a proxy for the CO2 emissions saved. The household income accounts for the specific economic conditions across different regions and the agent's age group, level of education, and household type. Finally, the influence of communication is measured by the number of links with other households that have already adopted a PV system. In each simulation step, the program dynamically updates the social system and the communication network, while the PV system's investment costs are revised according to a one-factor experience curve. The model's structure is applied for a case study based on the evolution of residential PV systems in Italy over the 2012–2026 period. The model's initial state is calibrated on the basis of the actual diffusion of residential PV in Italy over the 2006–2011 period. Our results show that, following Italy's new feed-in tariff scheme, domestic PV installations are already beyond an initial stage of rapid growth and, though likely to spread further, they will do so at a significantly slower rate of diffusion.

도시철도 시스템 전기요금 절감을 위한 혼합정수계획법 기반 ESS(에너지저장장치) 스케줄링 기법

도시철도 시스템 전기요금 절감을 위한 혼합정수계획법 기반 ESS(에너지저장장치) 스케줄링 기법

How to get doctors to hand hygiene: nudge nudge

The Nudge Theory has been widely used to improve healthy behavior [1,2] and works as an external cue to memory [3]. The Nudge Theory is based on behavioural economics models that has been applied to move communities towards rational targeted purchasing and ecological preferred behaviour patterns [1,2]. Consumers have been nudged successfully towards lower electricity purchasing patterns through displays of their past and current purchasing pattern.

Renewable Energy Capacity Limits in Kazakhstan: Critical Analysis of International 'Best Practices'

Following the feed-in tariff schemes implemented in the EU (and other parts of the world), Kazakhstan adopted a mechanism to support the use of renewable energy for electricity production. The scheme is based on the purchase of electricity at a guaranteed minimum price for a duration of fifteen years. The Kazakh Renewable Energy Law reinforces the stability of this long-term price guarantee by constraining the right of the government to introduce changes to the promised level of support: tariff changes can only be introduced once every three years and this only for new projects. At the same time, the Kazakh Renewable Energy Law does not explicitly limit the amount of renewable energy capacity that can benefit from support under the Kazakh feed-in tariff scheme. Against the background of the EU experience with the promotion of renewable energy, it is necessary to question how the Kazakh government could react to the excessive success of the support mechanism. In accordance with existing Kazakh law, what volume and tariff limits could the government introduce to restrict the number of projects eligible for support under the feed-in tariff scheme – and at the same time maintain investors’ confidence in the stability and predictability of Kazakh renewable energy policy? In the fine-tuning of Kazakh law on renewable energy, what lessons can Kazakhstan learn from the international experience with the promotion of renewable energy?

Optimal sizing of a hybrid grid-connected photovoltaic and wind power system

Abstract Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic–wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be economically profitable in the studied rural township in the Mediterranean climate region of central Catalonia (Spain), being the system paid off after 18 years of operation out of 25 years of system lifetime. Although the annual costs of the system are notably lower compared with the cost of electricity purchase, which is the current alternative, a significant upfront investment of over $10 M – roughly two thirds of total system lifetime cost – would be required to install such system.

Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes

The storage capacity of the batteries in an electric vehicle (EV) could be utilised to store electrical energy and give it back to the grid when needed by participating in vehicle to grid (V2G) schemes. This participation could be a source of revenue for vehicle owners thus reducing the total charging cost of their EVs. A V2G simulator has been developed using MATLAB to find out the potential cost saving from participation of EVs in V2G schemes. A standard IEEE30 network has been modelled in the simulator which uses the MATPOWER engine to undertake power flow analysis. A novel control algorithm has been developed to take advantage of the difference between the selling and buying electricity prices by charging and discharging EVs at the appropriate time. Two scenarios are simulated to compare the total charging cost of EVs with or without the utilisation of V2G technology within the power system assuming a total of 5000 EVs. The results of the simulation show that the applied control strategy with V2G is able to reduce the charging cost of EVs by 13.6 % while satisfying the minimum requirement for state of charge (SoC) of the EV batteries to complete their next journey.

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase

In a deregulated electricity market, congestion of the transmission lines is a major problem the independent system operator (ISO) would face. Rescheduling of generators is one of the most practiced techniques to alleviate the congestion. However, not all generators in the system operate deterministically and independently, especially wind power generators (WTGs). Therefore, a novel optimal rescheduling model for congestion management that accounts for the uncertain and correlated power sources and loads is proposed. A probabilistic power flow (PPF) model based on 2m+1 point estimate method (PEM) is used to simulate the performance of uncertain and correlated input random variables. In addition, the impact of direct electricity purchase contracts on the congestion management has also been studied. This paper uses artificial bee colony (ABC) algorithm to solve the complex optimization problem. The proposed algorithm is tested on modified IEEE 30-bus system and IEEE 57-bus system to demonstrate the impacts of the uncertainties and correlations of the input random variables and the direct electricity purchase contracts on the congestion management. Both pool and nodal pricing model are also discussed.

Optimization of steel production scheduling with complex time-sensitive electricity cost

Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.

Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs

Abstract With the consideration of time sequence characteristics of load and distributed generator (DG) output, a novel method is presented for optimal sitting and sizing of DG in distributed system. Multi-objective functions have been formulated with the consideration of minimum investment and operational cost of DG, minimum purchasing electricity cost from main grid and minimum voltage deviation. To solve the multi-objective optimization problem, an improved Non-dominated Sorting Genetic Algorithm II has been proposed. The compromised solution is extracted from the Pareto set using the fuzzy theory method. Several experiments have been made on the modified PG&E 69-bus and multiple actual test cases with the consideration of multiple DGs. The computational result and comparisons indicate the proposed method for optimal placement and sizing of DG is feasible and effective.

Promotion of biogas production in Latvia

Promotion of biogas production to generate energy is associated with several EU policy initiatives such as increasing the production of electricity and thermal energy, energy independence, and the reduction of GHG emissions. Besides, one of the topical problems is the increasing amount of organic waste and residue from agriculture, industry, and households, thereby making waste management, recycling, and waste reduction one of the government policy priorities. Therefore, energy production from biogas that forms in the fermentation process of various waste, including agricultural wastes, provides environmental, social, and economic gains, and it plays a significant role from both the bioeconomic and energy policy perspectives. To reach both the international and the national policy targets in renewable energy production, all EU member states use several support mechanisms. In Latvia four support mechanisms are used for production of electricity and heat from renewables – obligatory state purchase of electricity, guarantee of a set price, release from paying the electricity tax for the energy, produced from the renewable resources and public (EU structural fund) funding for investments, and one support mechanism for production of biofuel. In few recent years in Latvia, the support mechanisms described above have created a motivating support system for joining the biogas production sector, but due to the pressure of the manufacturing enterprises and households the government intends to change the support policy and the amount of the support for electricity production from biogas. It creates instability and increases political risks for biogas producers thus obstructing the development of the sector. The current article reviews the former and current support policy for biogas production in EU and Latvia and gives an insight to the significance of political risks in the production of biogas from agriculture biomass.

Modeling and design of a 25 MW osmotic power plant (PRO) on Bahmanshir River of Iran

Osmotic energy is one of the renewable energies. This osmotic energy source is easily obtainable all over the world where river water flows into the ocean or sea. In this article, Bahmanshir River in Iran which falls into the Persian Gulf was considered as selected place for modeling and design of 25 MW osmotic power plant in PRO method. Results indicates that for a 15 year return on investment, annual increase in purchase price of electricity 10% and interest rate 6%, sale price of electricity should be 0.4117 €/KWh which is highly expensive compared to state's suggested price of electricity from renewable source (i.e. 0.09 €/KWh).In order to balance purchase and sale price of electricity, unit price of intake and outfall, unit price of micro-filtration and interest rate should be reduced 9.1%, 15% and 25% respectively and Δλ should be increase 4.3%.

The Utilization of Scrap Tires as an Energy Source and Its Environmental Benefit Analysis in Taiwan

Taiwan, a country in the subtropics, heavily depends on imported energy with over 99% of its energy imported. In this regard, scrap tires for use as an energy source has thus became attractive in the past two decades. The objective of this article is to present a comprehensive analysis of tire-to-energy in Taiwan. This article gives a concise summary of the current status of scrap tire production and its recycling in recent years. It was found that a major scrap tire market in Taiwan (about three-quarters) is used as fuel in the cement, power, and paper and pulp utilities. Under the authorization of the Energy Management Law, the central competent authority has promulgated the regulations to encourage energy-intensive industries to participate in the cogeneration system through excess electricity purchase and financial incentives. Using the method recommended by the Intergovernmental Panel on Climate Change, the total energy potential and the environmental benefit of mitigating CO2 emissions in place of coal were preliminarily calculated to be around 1,920 TJ and 4.1 × 10 7 kg, respectively, based on the certified volume of scrap tire and its energy use proportion in 2009.

Optimisation of size and control of grid-connected storage under real time electricity pricing conditions

In this paper, a storage system has been considered to be added to a private electricity facility in order to reduce the electricity bill. This kind of system could make sense with a time-of-use (TOU) tariff (with two or three periods of different electricity price) or a real-time pricing (RTP) tariff (which can vary hourly depending on wholesale market), where each day, electricity is bought from the AC grid during off-peak hours (at low price) to store energy, and during on-peak hours (at high price), the storage supplies the whole load or a part, avoiding the purchase of expensive electricity from the AC grid. A new optimisation model to obtain the best storage size and control of the charge–discharge process under RTP tariff has been developed, minimising the net present cost of the system. This methodology allows the electricity consumer to evaluate the profitability of adding storage. A case study of battery storage with a hypothetical hourly RTP tariff based on the Spanish electricity market pool of 2013 is shown. Considering the cost of batteries per kWh cycled expected in the near future (0.045€/kWhcycled), even the optimal storage size with the optimal control is not profitable compared to the system without storage. A much lower battery cost (0.0225€/kWhcycled) would be needed so that the storage system could be economically profitable. Considering a stretched RTP hourly pricing tariff (hourly prices higher than the average of each day multiplied by 1.3 and the lower ones by 0.7), the storage system with the costs expected in the near future (0.045€/kWhcycled) is profitable. We conclude that an hourly tariff that takes the base of the Spanish electricity market pool is not adequate to stimulate this technology, as the difference between maximum and minimum prices during the day is too low to compensate the capital cost plus replacement, operation, and maintenance of the storage system. In other countries, with much higher difference between minimum and maximum electricity price of the day, present battery cost could make it profitable.

Exploring the effects of adaptive reactive support capabilities by DGs in optimal operational scheduling of smart active distribution networks

This paper proposes an optimal day-ahead DA operational scheduling framework to be implemented in distribution management systems DMSs as the core of decision makings in active distribution networks ADNs. Belonging to the new emerging and prosperous technology namely smart distribution grids, the contextualized ADN is composed of active elements including both renewable-based and conventional distributed generations DGs as well as demand side management programs. Also, the information and communication technology ICT infrastructures are well-equipped in the network which enriches the distribution system operator DSO to have a remote and online control on active elements whenever needed. The proposed day-ahead optimal operational framework firstly schedules the next 24-hour dispatches of DGs, responsive loads RLs as well as electricity purchase from wholesale market aiming to minimize the total operation costs. In the first strategy, DGs are supposed to be operated within the mandatory range of reactive support without any financial compensation. Subsequently as an innovative point, the costs of reactive power purchases from both wholesale market and active elements of ADN are judicially included in the scheduling process wherein DGs are contemplated to be utilized in adaptive power factor mode up to a pre-specified minimum value considering financial reimbursements. In contradiction to the conventional fixed pricing mechanisms, a more practical approach for reactive support of DGs is considered and the effect of higher participation of active elements in reactive power provision are thoroughly interrogated in enhancing the economical and technical issues. Also, having a proper control on DGs operating power factor has resulted in extra released capacity which could be exploited to cover the network uncertainties such as wind speed or load variation during a day. The established model is formulated as a mixed integer non-linear problem and solved using binary genetic algorithm. A 33-bus ADN is considered to verify the performance of the proposed optimal operation framework.

Mathematic formulas for calculating the prices for selling and buying electric energy in Bulgaria

The paper presents the mechanism for determination of the prices at which the electricity Suppliers of last resort (EVN, CEZ and Energo-pro) buy electricity from the National electric company (NEC) and then sell it to final consumers. The Rules for trade with electrical energy (RTEE) in Bulgaria; the role of the Public energy supplier (the National Electric company) in determining energy prices and the roles of the State Energy and Water Regulatory Commission (SEWRC) in determining the forecasted quantities of electric energy that are subject of compulsory purchase as well as in determining the quantities that are not subject of compulsory purchase within the limits of the availability determined according to the Bulgarian Energy Act have been outlined in the research. The mathematic formulas used by SEWRC in determining energy prices and a method for determining of the non-balances between the members of the Special balancing group of one of the Suppliers of last resort in Bulgaria have been presented.

ENERGY MANAGEMENT IN AN INDUSTRIAL TOWN USING THE CONCEPT OF VIRTUAL POWER PLANTS

Abstract. Utilization of micro networks is highly noticed in recent years. Surveyed in this article, is a small network containing wind and solar power plants, fuel cells, super capacitors, batteries, and so forth. The network required load during 24 hours is supplied by available resources in a way that the cost would be minimum. The provisions related to CHP generators, wind power plants, batteries, heat storage, heat exchanger, and load supplying in the network are considered. In order to make the issue simpler, linearization method is used to convert the first and second degree equations. The problem is solved using GAMS software. The numeric study is done in two scenarios without concerning the pollution cost. Results showed in the case pollution cost is considered, the tendency to purchase electricity from the electricity market will increase. In addition, storages utilization leads to reduction of the network load ratio.

Economic optimization of smart distribution networks considering real-time pricing

With the development of smart meters, a real-time pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.